diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml new file mode 100644 index 0000000..a4addbe --- /dev/null +++ b/.github/workflows/ci.yml @@ -0,0 +1,90 @@ +name: ci + +on: + push: + branches: + - master + pull_request: + branches: + - master + +# https://jacobian.org/til/github-actions-poetry/ +jobs: + flake8-lint: + runs-on: ubuntu-latest + name: Lint + steps: + - name: Check out source repository + uses: actions/checkout@v2 + - name: Set up Python environment + uses: actions/setup-python@v1 + with: + python-version: "3.9" + - name: flake8 Lint + uses: py-actions/flake8@v1 + with: + ignore: "E203,E402,E501,F401,F841" + exclude: "logs/*,data/*,furuta/logging/protobuf/*" + + test: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v2 + + # If you wanted to use multiple Python versions, you'd have specify a matrix in the job and + # reference the matrixe python version here. + - uses: actions/setup-python@v2 + with: + python-version: 3.9 + + # Cache the installation of Poetry itself, e.g. the next step. This prevents the workflow + # from installing Poetry every time, which can be slow. Note the use of the Poetry version + # number in the cache key, and the "-0" suffix: this allows you to invalidate the cache + # manually if/when you want to upgrade Poetry, or if something goes wrong. This could be + # mildly cleaner by using an environment variable, but I don't really care. + - name: cache poetry install + uses: actions/cache@v2 + with: + path: ~/.local + key: poetry-1.4.0-0 + + # Install Poetry. You could do this manually, or there are several actions that do this. + # `snok/install-poetry` seems to be minimal yet complete, and really just calls out to + # Poetry's default install script, which feels correct. I pin the Poetry version here + # because Poetry does occasionally change APIs between versions and I don't want my + # actions to break if it does. + # + # The key configuration value here is `virtualenvs-in-project: true`: this creates the + # venv as a `.venv` in your testing directory, which allows the next step to easily + # cache it. + - uses: snok/install-poetry@v1 + with: + version: 1.4.0 + virtualenvs-create: true + virtualenvs-in-project: true + + # Cache your dependencies (i.e. all the stuff in your `pyproject.toml`). Note the cache + # key: if you're using multiple Python versions, or multiple OSes, you'd need to include + # them in the cache key. I'm not, so it can be simple and just depend on the poetry.lock. + - name: cache deps + id: cache-deps + uses: actions/cache@v2 + with: + path: .venv + key: pydeps-${{ hashFiles('**/poetry.lock') }} + + # Install dependencies. `--no-root` means "install all dependencies but not the project + # itself", which is what you want to avoid caching _your_ code. The `if` statement + # ensures this only runs on a cache miss. + - run: poetry install --no-interaction --no-root + if: steps.cache-deps.outputs.cache-hit != 'true' + + # Now install _your_ project. This isn't necessary for many types of projects -- particularly + # things like Django apps don't need this. But it's a good idea since it fully-exercises the + # pyproject.toml and makes that if you add things like console-scripts at some point that + # they'll be installed and working. + - run: poetry install --no-interaction + + # And finally run tests. I'm using pytest and all my pytest config is in my `pyproject.toml` + # so this line is super-simple. But it could be as complex as you need. + - run: poetry run pytest diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml deleted file mode 100644 index 4fc7fc3..0000000 --- a/.github/workflows/lint.yml +++ /dev/null @@ -1,26 +0,0 @@ -name: flake8 Lint - -on: - push: - branches: - - master - pull_request: - branches: - - master - -jobs: - flake8-lint: - runs-on: ubuntu-latest - name: Lint - steps: - - name: Check out source repository - uses: actions/checkout@v2 - - name: Set up Python environment - uses: actions/setup-python@v1 - with: - python-version: "3.8" - - name: flake8 Lint - uses: py-actions/flake8@v1 - with: - ignore: "E203,E402,E501,F401,F841" - exclude: "logs/*,data/*,furuta_gym/logging/protobuf/*" diff --git a/.gitignore b/.gitignore index fd1709d..ebff421 100644 --- a/.gitignore +++ b/.gitignore @@ -1,4 +1,5 @@ **/videos/* +**/outputs/* **/wandb/* **/runs/* **/models/* @@ -124,7 +125,7 @@ celerybeat.pid # Environments .env .venv -env/ +# env/ bc we use env for gym env config but have no env/ dir for actual venvs venv/ ENV/ env.bak/ diff --git a/furuta/__init__.py b/furuta/__init__.py index c7a6b36..7e3162b 100644 --- a/furuta/__init__.py +++ b/furuta/__init__.py @@ -2,9 +2,9 @@ register( id="FurutaReal-v0", - entry_point="furuta_gym.envs:FurutaReal", + entry_point="furuta.rl.envs.furuta_real:FurutaReal", ) register( id="FurutaSim-v0", - entry_point="furuta_gym.envs:FurutaSim", + entry_point="furuta.rl.envs.furuta_sim:FurutaSim", ) diff --git a/furuta/controls/controllers.py b/furuta/controls/controllers.py index ad3160c..f039f4c 100644 --- a/furuta/controls/controllers.py +++ b/furuta/controls/controllers.py @@ -11,11 +11,15 @@ def compute_command(self, position: float): @staticmethod def build_controller(parameters: dict): controller_type = parameters["controller_type"] - match controller_type: - case "PIDController": - return PIDController(parameters) - case _: - raise ValueError(f"Invalid controller type: {controller_type}") + # match controller_type: + # case "PIDController": + # return PIDController(parameters) + # case _: + # raise ValueError(f"Invalid controller type: {controller_type}") + if controller_type == "PIDController": + return PIDController(parameters) + else: + raise ValueError(f"Invalid controller type: {controller_type}") class PIDController(Controller): diff --git a/furuta/logging/protobuf/pendulum_state.proto b/furuta/logging/protobuf/pendulum_state.proto index 1ca63a5..69d18ce 100644 --- a/furuta/logging/protobuf/pendulum_state.proto +++ b/furuta/logging/protobuf/pendulum_state.proto @@ -6,7 +6,5 @@ message PendulumState { float motor_angle_velocity = 3; float pendulum_angle_velocity = 4; float reward = 5; - bool done = 6; - float action = 7; - float corrected_action = 8; + float action = 6; } diff --git a/furuta/logging/protobuf/pendulum_state_pb2.py b/furuta/logging/protobuf/pendulum_state_pb2.py index 3cdfa87..a1025db 100644 --- a/furuta/logging/protobuf/pendulum_state_pb2.py +++ b/furuta/logging/protobuf/pendulum_state_pb2.py @@ -1,25 +1,26 @@ +# -*- coding: utf-8 -*- # Generated by the protocol buffer compiler. DO NOT EDIT! # source: pendulum_state.proto """Generated protocol buffer code.""" +from google.protobuf.internal import builder as _builder from google.protobuf import descriptor as _descriptor from google.protobuf import descriptor_pool as _descriptor_pool from google.protobuf import symbol_database as _symbol_database -from google.protobuf.internal import builder as _builder - # @@protoc_insertion_point(imports) _sym_db = _symbol_database.Default() -DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile( - b'\n\x14pendulum_state.proto"\xc3\x01\n\rPendulumState\x12\x13\n\x0bmotor_angle\x18\x01 \x01(\x02\x12\x16\n\x0ependulum_angle\x18\x02 \x01(\x02\x12\x1c\n\x14motor_angle_velocity\x18\x03 \x01(\x02\x12\x1f\n\x17pendulum_angle_velocity\x18\x04 \x01(\x02\x12\x0e\n\x06reward\x18\x05 \x01(\x02\x12\x0c\n\x04\x64one\x18\x06 \x01(\x08\x12\x0e\n\x06\x61\x63tion\x18\x07 \x01(\x02\x12\x18\n\x10\x63orrected_action\x18\x08 \x01(\x02\x62\x06proto3' -) -_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals()) -_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, "pendulum_state_pb2", globals()) + +DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x14pendulum_state.proto\"\x9b\x01\n\rPendulumState\x12\x13\n\x0bmotor_angle\x18\x01 \x01(\x02\x12\x16\n\x0ependulum_angle\x18\x02 \x01(\x02\x12\x1c\n\x14motor_angle_velocity\x18\x03 \x01(\x02\x12\x1f\n\x17pendulum_angle_velocity\x18\x04 \x01(\x02\x12\x0e\n\x06reward\x18\x05 \x01(\x02\x12\x0e\n\x06\x61\x63tion\x18\x06 \x01(\x02\x62\x06proto3') + +_globals = globals() +_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals) +_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'pendulum_state_pb2', _globals) if _descriptor._USE_C_DESCRIPTORS == False: - DESCRIPTOR._options = None - _PENDULUMSTATE._serialized_start = 25 - _PENDULUMSTATE._serialized_end = 220 + DESCRIPTOR._options = None + _globals['_PENDULUMSTATE']._serialized_start=25 + _globals['_PENDULUMSTATE']._serialized_end=180 # @@protoc_insertion_point(module_scope) diff --git a/furuta/rl/__init__.py b/furuta/rl/__init__.py index d6191b9..e69de29 100644 --- a/furuta/rl/__init__.py +++ b/furuta/rl/__init__.py @@ -1,3 +0,0 @@ -# from furuta_gym.envs.furuta_base import FurutaBase # noqa F420 -# from furuta_gym.envs.furuta_real import FurutaReal # noqa F420 -# from furuta_gym.envs.furuta_sim import FurutaSim # noqa F420 diff --git a/furuta/rl/algos.py b/furuta/rl/algos.py index 552d8fa..8f91ba0 100644 --- a/furuta/rl/algos.py +++ b/furuta/rl/algos.py @@ -6,11 +6,11 @@ # check if they all have the train freq param # check if they have other tuple args # check if it would be cleaner for sb3 to accept list instead of tuple? -# TODO also does having the sb3 import means i need sb3 when importing from everywhere else in the package? class SAC(stable_baselines3.SAC): def __init__(self, **kwargs): # sb3 expects tuple, omegaconf returns list # so we need to convert kwarg train_freq from tuple to list - kwargs.update({"train_freq": tuple(kwargs["train_freq"])}) + if "train_freq" in kwargs and isinstance(kwargs["train_freq"], list): + kwargs.update({"train_freq": tuple(kwargs["train_freq"])}) super().__init__(**kwargs) diff --git a/furuta/rl/envs/furuta_base.py b/furuta/rl/envs/furuta_base.py index bdd4246..db1d4af 100644 --- a/furuta/rl/envs/furuta_base.py +++ b/furuta/rl/envs/furuta_base.py @@ -1,26 +1,18 @@ -from collections import namedtuple -from dataclasses import dataclass -from math import cos, sin +from typing import Optional -import gym +import gymnasium as gym import numpy as np -from gym.spaces import Box +from gymnasium.spaces import Box from furuta.utils import ALPHA, ALPHA_DOT, THETA, THETA_DOT, Timing def alpha_reward(state): - try: - rwd = (1 + -cos(state[ALPHA])) / 2 - except Exception as e: - print(e) - print(state) - - return rwd + return (1 + -np.cos(state[ALPHA])) / 2 def alpha_theta_reward(state): - return alpha_reward(state) + (1 + cos(state[THETA])) / 2 + return alpha_reward(state) + (1 + np.cos(state[THETA])) / 2 REWARDS = { @@ -30,92 +22,93 @@ def alpha_theta_reward(state): class FurutaBase(gym.Env): - metadata = {"render.modes": ["rgb_array"]} # TODO add headless mode - - def __init__(self, fs, reward, state_limits=None): - - self.timing = Timing(fs) - self.viewer = None + metadata = { + "render_modes": ["rgb_array", "human"], + "render_fps": 50, # TODO should this be the same as the control freq/sim dt? + } + + def __init__( + self, + control_freq, + reward, + angle_limits=[np.pi, np.pi], # used to help convergence? + speed_limits=[60, 400], # used to avoid damaging the real robot or diverging sim + render_mode="rgb_array", + ): + self.render_mode = render_mode + + self.timing = Timing(control_freq) self._state = None self.reward = reward - self._reward_func = REWARDS[self.reward.name] + self.screen_width = 600 + self.screen_height = 400 + self.screen = None + self.clock = None + + self._reward_func = REWARDS[self.reward] act_max = np.array([1.0], dtype=np.float32) - if state_limits: - self.state_max = np.array(state_limits, dtype=np.float32) - else: - self.state_max = np.array([np.inf, np.inf, np.inf, np.inf], dtype=np.float32) + if angle_limits is None: + angle_limits = [np.inf, np.inf] + if speed_limits is None: + speed_limits = [np.inf, np.inf] - obs_max = np.array( - [1.0, 1.0, 1.0, 1.0, self.state_max[2], self.state_max[3]], dtype=np.float32 + self.state_max = np.array( + [angle_limits[0], angle_limits[1], speed_limits[0], speed_limits[1]], dtype=np.float32 ) + # max obs based on max speeds measured on the robot + # in sim the speeds spike at 30 rad/s when trained + # selected 50 rad/s to be safe bc its probably higher during training + # it's also ok if the speeds exceed theses values as we only use them for rescaling + # and it's okay if the nn sees values a little bit above 1 + # obs is [cos(th), sin(th), cos(al), sin(al), th_d, al_d)] + obs_max = np.array([1.0, 1.0, 1.0, 1.0, 50, 50], dtype=np.float32) + # Spaces self.state_space = Box( - # labels=('theta', 'alpha', 'theta_dot', 'alpha_dot'), + # ('theta', 'alpha', 'theta_dot', 'alpha_dot'), low=-self.state_max, high=self.state_max, dtype=np.float32, ) self.observation_space = Box( - # labels=('cos_th', 'sin_th', 'cos_al', 'sin_al', 'th_d', 'al_d'), + # ('cos_th', 'sin_th', 'cos_al', 'sin_al', 'th_d', 'al_d'), low=-obs_max, high=obs_max, dtype=np.float32, ) self.action_space = Box( - # labels=('action',), + # ('action',), low=-act_max, high=act_max, dtype=np.float32, ) - @profile def step(self, action): - # TODO this is slow, do we even need it? - # sb3 knows the env action space, probably it won't pass invalid actions - - # assert a is not None, "Action should be not None" - # assert isinstance(a, np.ndarray), "The action should be a ndarray" - # assert np.all(not np.isnan(a)), "Action NaN is not a valid action" - # assert a.ndim == 1, "The action = {a} must be 1d but the input is {a.ndim}d" - # err_msg = f"{action!r} ({type(action)}) invalid" - - # assert self.action_space.contains(action), "Action is not in action space" - # assert self._state is not None, "Call reset before using step method." - # first read the robot/sim state rwd = self._reward_func(self._state) obs = self.get_obs() - info = { - "motor_angle": float(self._state[THETA]), - "pendulum_angle": float(self._state[ALPHA]), - "motor_angle_velocity": float(self._state[THETA_DOT]), - "pendulum_angle_velocity": float(self._state[ALPHA_DOT]), - "reward": float(rwd), - "action": float(action), - } - - # then take action + # then take action/step the sim self._update_state(action[0]) - done = not self.state_space.contains(self._state) - if done: - rwd -= self.reward.oob_penalty + terminated = not self.state_space.contains(self._state) - info["done"] = bool(done) + # if terminated: + # rwd -= self.reward.oob_penalty - return obs, rwd, done, info + truncated = False + + return obs, rwd, terminated, truncated, {} def get_obs(self): return np.float32( [ - # TODO maybe call cos, sin at once? save a bit of time np.cos(self._state[THETA]), np.sin(self._state[THETA]), np.cos(self._state[ALPHA]), @@ -125,174 +118,112 @@ def get_obs(self): ] ) - def reset(self): - raise NotImplementedError + def reset( + self, + seed: Optional[int] = None, + options: Optional[dict] = None, + ): + super().reset(seed=seed) def _update_state(self, a): raise NotImplementedError - def render(self, mode="rgb_array"): - if self.viewer is None: - self.viewer = CartPoleSwingUpViewer(world_width=(2 * np.pi)) + def render(self): + # https://github.com/Farama-Foundation/Gymnasium/blob/6baf8708bfb08e37ce3027b529193169eaa230fd/gymnasium/envs/classic_control/cartpole.py#L229 + if self.render_mode is None: + assert self.spec is not None + gym.logger.warn( + "You are calling render method without specifying any render mode. " + "You can specify the render_mode at initialization, " + f'e.g. gym.make("{self.spec.id}", render_mode="rgb_array")' + ) + return + + import pygame + from pygame import gfxdraw + + if self.screen is None: + pygame.init() + if self.render_mode == "human": + pygame.display.init() + self.screen = pygame.display.set_mode((self.screen_width, self.screen_height)) + else: # mode == "rgb_array" + self.screen = pygame.Surface((self.screen_width, self.screen_height)) + if self.clock is None: + self.clock = pygame.time.Clock() + + world_width = 2 * np.pi + scale = self.screen_width / world_width + polewidth = 10.0 + polelen = scale * (2 * 0.5) # TODO use the right pole len? + cartwidth = 50.0 + cartheight = 30.0 if self._state is None: return None - self.viewer.update(self._state) - return self.viewer.render(return_rgb_array=mode == "rgb_array") - - -@dataclass(frozen=True) -class CartParams: - """Parameters defining the Cart.""" - - width: float = 1 / 3 - height: float = 1 / 6 - mass: float = 0.5 - - -@dataclass(frozen=True) -class PoleParams: - """Parameters defining the Pole.""" - - width: float = 0.05 - length: float = 0.6 - mass: float = 0.5 + x = self._state + self.surf = pygame.Surface((self.screen_width, self.screen_height)) + self.surf.fill((255, 255, 255)) -Screen = namedtuple("Screen", "width height") + l, r, t, b = -cartwidth / 2, cartwidth / 2, cartheight / 2, -cartheight / 2 + axleoffset = cartheight / 4.0 + # make sure theta stays between -pi and pi + theta = (x[THETA] % (2 * np.pi)) - np.pi + cartx = theta * scale + self.screen_width / 2.0 # MIDDLE OF CART + carty = 100 # TOP OF CART + cart_coords = [(l, b), (l, t), (r, t), (r, b)] + cart_coords = [(c[0] + cartx, c[1] + carty) for c in cart_coords] + gfxdraw.aapolygon(self.surf, cart_coords, (0, 0, 0)) + gfxdraw.filled_polygon(self.surf, cart_coords, (0, 0, 0)) -class CartPoleSwingUpViewer: - """Class that encapsulates all the variables and objectecs needed to render a - CartPoleSwingUpEnv. - - It handles all the initialization and updating of each object on screen and handles calls to - the underlying gym.envs.classic_control.rendering.Viewer instance. - """ - - screen = Screen(width=600, height=400) - - def __init__(self, world_width): - # TODO: make sure that's not redundant - import pyglet - from gym.envs.classic_control import rendering - - pyglet.options["headless"] = True # noqa: F821 - - cart = CartParams() - pole = PoleParams() - - self.cart = cart - self.pole = pole - - self.world_width = world_width - screen = self.screen - scale = screen.width / self.world_width - cartwidth, cartheight = scale * cart.width, scale * cart.height - polewidth, polelength = scale * pole.width, scale * pole.length - self.viewer = rendering.Viewer(screen.width, screen.height) - self.transforms = { - "cart": rendering.Transform(), - "pole": rendering.Transform(translation=(0, 0)), - "pole_bob": rendering.Transform(), - "wheel_l": rendering.Transform(translation=(-cartwidth / 2, -cartheight / 2)), - "wheel_r": rendering.Transform(translation=(cartwidth / 2, -cartheight / 2)), - } - - self._init_track(rendering, cartheight) - self._init_cart(rendering, cartwidth, cartheight) - self._init_wheels(rendering, cartheight) - self._init_pole(rendering, polewidth, polelength) - self._init_axle(rendering, polewidth) - # Make another circle on the top of the pole - self._init_pole_bob(rendering, polewidth) - - def _init_track(self, rendering, cartheight): - screen = self.screen - carty = screen.height / 2 - track_height = carty - cartheight / 2 - cartheight / 4 - track = rendering.Line((0, track_height), (screen.width, track_height)) - track.set_color(0, 0, 0) - self.viewer.add_geom(track) - - def _init_cart(self, rendering, cartwidth, cartheight): - lef, rig, top, bot = ( - -cartwidth / 2, - cartwidth / 2, - cartheight / 2, - -cartheight / 2, - ) - cart = rendering.FilledPolygon([(lef, bot), (lef, top), (rig, top), (rig, bot)]) - cart.add_attr(self.transforms["cart"]) - cart.set_color(1, 0, 0) - self.viewer.add_geom(cart) - - def _init_pole(self, rendering, polewidth, polelength): - lef, rig, top, bot = ( + l, r, t, b = ( -polewidth / 2, polewidth / 2, - polelength - polewidth / 2, + polelen - polewidth / 2, -polewidth / 2, ) - pole = rendering.FilledPolygon([(lef, bot), (lef, top), (rig, top), (rig, bot)]) - pole.set_color(0, 0, 1) - pole.add_attr(self.transforms["pole"]) - pole.add_attr(self.transforms["cart"]) - self.viewer.add_geom(pole) - - def _init_axle(self, rendering, polewidth): - axle = rendering.make_circle(polewidth / 2) - axle.add_attr(self.transforms["pole"]) - axle.add_attr(self.transforms["cart"]) - axle.set_color(0.1, 1, 1) - self.viewer.add_geom(axle) - - def _init_pole_bob(self, rendering, polewidth): - pole_bob = rendering.make_circle(polewidth / 2) - pole_bob.add_attr(self.transforms["pole_bob"]) - pole_bob.add_attr(self.transforms["pole"]) - pole_bob.add_attr(self.transforms["cart"]) - pole_bob.set_color(0, 0, 0) - self.viewer.add_geom(pole_bob) - - def _init_wheels(self, rendering, cartheight): - wheel_l = rendering.make_circle(cartheight / 4) - wheel_r = rendering.make_circle(cartheight / 4) - wheel_l.add_attr(self.transforms["wheel_l"]) - wheel_l.add_attr(self.transforms["cart"]) - wheel_r.add_attr(self.transforms["wheel_r"]) - wheel_r.add_attr(self.transforms["cart"]) - wheel_l.set_color(0, 0, 0) # Black, (B, G, R) - wheel_r.set_color(0, 0, 0) # Black, (B, G, R) - self.viewer.add_geom(wheel_l) - self.viewer.add_geom(wheel_r) - - def update(self, state): - """Updates the positions of the objects on screen.""" - screen = self.screen - scale = screen.width / self.world_width - - th, al, _, _ = state - al = (al - np.pi) % (2 * np.pi) # change angle origin - - # keep th between -pi and pi - th = (th + np.pi) % (2 * np.pi) - np.pi - - # use motor angle (theta) as cart x position - cartx = th * scale + screen.width / 2.0 # MIDDLE OF CART - carty = screen.height / 2 - - self.transforms["cart"].set_translation(cartx, carty) - self.transforms["pole"].set_rotation(al) - self.transforms["pole_bob"].set_translation( - -self.pole.length * np.sin(al), self.pole.length * np.cos(al) + + pole_coords = [] + for coord in [(l, b), (l, t), (r, t), (r, b)]: + coord = pygame.math.Vector2(coord).rotate_rad(x[ALPHA] + np.pi) + coord = (coord[0] + cartx, coord[1] + carty + axleoffset) + pole_coords.append(coord) + gfxdraw.aapolygon(self.surf, pole_coords, (202, 152, 101)) + gfxdraw.filled_polygon(self.surf, pole_coords, (202, 152, 101)) + + gfxdraw.aacircle( + self.surf, + int(cartx), + int(carty + axleoffset), + int(polewidth / 2), + (129, 132, 203), + ) + gfxdraw.filled_circle( + self.surf, + int(cartx), + int(carty + axleoffset), + int(polewidth / 2), + (129, 132, 203), ) - def render(self, *args, **kwargs): - """Forwards the call to the underlying Viewer instance.""" - return self.viewer.render(*args, **kwargs) + gfxdraw.hline(self.surf, 0, self.screen_width, carty, (0, 0, 0)) + + self.surf = pygame.transform.flip(self.surf, False, True) + self.screen.blit(self.surf, (0, 0)) + if self.render_mode == "human": + pygame.event.pump() + self.clock.tick(self.metadata["render_fps"]) + pygame.display.flip() + + elif self.render_mode == "rgb_array": + return np.transpose(np.array(pygame.surfarray.pixels3d(self.screen)), axes=(1, 0, 2)) def close(self): - """Closes the underlying Viewer instance.""" - self.viewer.close() + if self.screen is not None: + import pygame + + pygame.display.quit() + pygame.quit() diff --git a/furuta/rl/envs/furuta_real.py b/furuta/rl/envs/furuta_real.py index f4e9af7..ea019c8 100644 --- a/furuta/rl/envs/furuta_real.py +++ b/furuta/rl/envs/furuta_real.py @@ -1,117 +1,90 @@ -import configparser import logging from time import sleep +from typing import Optional import numpy as np -from furuta.common import VelocityFilter -from furuta.envs.furuta_base import FurutaBase -from furuta.envs.hardware.robot import FurutaRobot +from furuta.rl.envs.furuta_base import FurutaBase +from furuta.robot import Robot +from furuta.utils import ALPHA, ALPHA_DOT, THETA, THETA_DOT, VelocityFilter + +MAX_RESET_TIME = 7 # seconds +RESET_TIME = 0.5 +ALPHA_THRESH = np.deg2rad( + 2 +) # alpha should stay between -2 and 2 deg for 0.5 sec for us to consider the env reset class FurutaReal(FurutaBase): def __init__( self, - fs=100, - fs_ctrl=100, - action_limiter=False, - safety_th_lim=1.5, - reward="simple", - state_limits="low", - config_file="robot.ini", + control_freq=100, + reward="alpha_theta", + angle_limits=None, + speed_limits=None, + usb_device="/dev/ttyACM0", ): - super().__init__(fs, fs_ctrl, action_limiter, safety_th_lim, reward, state_limits) - - self.robot = FurutaRobot() + super().__init__(control_freq, reward, angle_limits, speed_limits) - self.vel_filt = VelocityFilter(2, dt=self.timing.dt) + self.robot = Robot(usb_device) - self._state = self._read_state() - - def _update_state(self, action): - self.robot.set_motor_voltage(action[0]) + self._init_vel_filt() - state = self._read_state() + self._update_state(0.0) - return state - - def _read_state(self): - # pendulum_deg_per_count = 360/self.pendulum_CPR - # p_count = self.pendulum_enc.readCounter() - # # p_count_modulo = p_count % self.pendulum_CPR - # pendulum_angle = pendulum_deg_per_count * p_count - # # pendulum_angle = (pendulum_angle + 180) % 360 - # pendulum_angle = pendulum_angle * np.pi / 180 - - # motor_deg_per_count = 360/self.motor_CPR - # m_count = self.motor_enc.readCounter() - # m_count_modulo = m_count % self.motor_CPR - # motor_angle = m_count_modulo * motor_deg_per_count - # motor_angle = (motor_angle + 180) % 360 - 180 - # motor_angle = motor_angle * np.pi / 180 + def _init_vel_filt(self): + self.vel_filt = VelocityFilter(2, dt=self.timing.dt) - motor_angle, pendulum_angle = self.robot.read_state() + def _update_state(self, action): + motor_angle, pendulum_angle = self.robot.step(action) # motor_angle: theta, pendulum angle: alpha pos = np.array([motor_angle, pendulum_angle], dtype=np.float32) - vel = self.vel_filt(pos) # TODO understand unit: rad/s ? + vel = self.vel_filt(pos) state = np.concatenate([pos, vel]) + self._state = state - return state - - def get_state(self): - return self._state - - def _reset_pendulum(self, tolerance=10, still_time=1, clear=True): - pass - - def reset(self): + def reset( + self, + seed: Optional[int] = None, + options: Optional[dict] = None, + ): + super().reset(seed=seed) logging.info("Reset env...") - # reset pendulum - self._reset_pendulum(40, 0.5, False) - # reset motor - logging.debug("Reset motor") - while True: - state = self._read_state() * 180 / np.pi - motor_angle = state[0] - motor_speed = state[2] - - speed = 0.3 - if abs(motor_angle) < 10: - - if abs(motor_speed) > 10: - # braking - if motor_angle <= 0: - self.motor.set_speed(-0.3) - elif motor_angle > 0: - self.motor.set_speed(0.3) - - sleep(10 / 100) - self.motor.set_speed(0) + # wait for pendulum to fall back to start position + reset_time = 0 + time_under_thresh = 0 - self.motor.set_speed(0) + while time_under_thresh < RESET_TIME and reset_time < MAX_RESET_TIME: + sleep(0.01) + if abs(self._state[ALPHA]) < ALPHA_THRESH: + time_under_thresh += 0.01 + else: + time_under_thresh = 0 + reset_time += 0.01 + self._update_state(0.0) - break - elif motor_angle >= 0: - self.motor.set_speed(-speed) - elif motor_angle < 0: - self.motor.set_speed(speed) + if reset_time >= MAX_RESET_TIME: + logging.error("Reset timeout") - # wait for pendulum to reset to start position - self._reset_pendulum(10, 1, True) + # reset both encoder, motor back to pos=0 + self.robot.reset_encoders() logging.info("Reset done") - self._state = self._read_state() - return self.get_obs() + self._update_state(0.0) + # else the first computed velocity will take into account previous episode + # and it'll be huge and wrong and will terminate the episode + self._init_vel_filt() + return self.get_obs(), {} # TODO: override parent render function # replace by taking webcam snapshot # and outputing rgb array? # but webcam can't record at 100hz + # or could just use the same render function!!! # def render(self, mode='human'): # raise NotImplementedError def close(self): - # self.motor.close() - self.robot.arduino.close() + self.robot.close() diff --git a/furuta/rl/envs/furuta_sim.py b/furuta/rl/envs/furuta_sim.py index a6ae5e4..01d0231 100644 --- a/furuta/rl/envs/furuta_sim.py +++ b/furuta/rl/envs/furuta_sim.py @@ -1,9 +1,9 @@ -from math import cos, sin +from typing import List, Optional, Union -import gym +import gymnasium as gym import numpy as np -from numpy.linalg import inv +from furuta.robot import QubeDynamics from furuta.utils import ALPHA, ALPHA_DOT, THETA, THETA_DOT, VelocityFilter from .furuta_base import FurutaBase @@ -12,23 +12,27 @@ class FurutaSim(FurutaBase): def __init__( self, - fs=50, + dyn: QubeDynamics = QubeDynamics(), + control_freq=50, reward="alpha", - state_limits=None, - sim_params=None, - encoders_CPRs=None, + angle_limits=None, + speed_limits=None, + encoders_CPRs: Optional[List[float]] = None, velocity_filter: int = None, + render_mode="rgb_array", ): - super().__init__(fs, reward, state_limits) - self.dyn = QubeDynamics() - if sim_params: - self.dyn.params = sim_params + super().__init__(control_freq, reward, angle_limits, speed_limits, render_mode) + self.dyn = dyn self.encoders_CPRs = encoders_CPRs - if velocity_filter: - self.vel_filt = VelocityFilter(velocity_filter, dt=self.timing.dt) + self.velocity_filter = velocity_filter + self._init_vel_filt() + + def _init_vel_filt(self): + if self.velocity_filter: + self.vel_filt = VelocityFilter(self.velocity_filter, dt=self.timing.dt) else: self.vel_filt = None @@ -45,11 +49,8 @@ def _init_state(self): # and maybe it should have a min voltage as well? # self._state = np.random.rand(4) # self.state_space.sample() self._simulation_state = 0.01 * np.float32(np.random.randn(self.state_space.shape[0])) - self._state = np.zeros(self.state_space.shape[0]) - - self._update_state(0) + self._state = self._simulation_state.copy() - @profile def _update_state(self, a): # ok so we simulate two things: the systems's state # and the way we would measure it @@ -57,7 +58,7 @@ def _update_state(self, a): # update the simulation state thdd, aldd = self.dyn(self._simulation_state, a) - # integrate + # TODO integrate self._simulation_state[ALPHA_DOT] += self.timing.dt * aldd self._simulation_state[THETA_DOT] += self.timing.dt * thdd self._simulation_state[ALPHA] += self.timing.dt * self._simulation_state[ALPHA_DOT] @@ -68,12 +69,12 @@ def _update_state(self, a): # do this by rounding _simulation_state[THETA/ALPHA] to the nearest multiple of 2pi/CPRs if self.encoders_CPRs: # TODO dedupe code here - theta_increment = 2 * np.pi / self.encoders_CPRs["motor_encoder_CPRs"] + theta_increment = 2 * np.pi / self.encoders_CPRs[THETA] self._state[THETA] = ( np.round(self._simulation_state[THETA] / theta_increment) * theta_increment ) - alpha_increment = 2 * np.pi / self.encoders_CPRs["pendulum_encoder_CPRs"] + alpha_increment = 2 * np.pi / self.encoders_CPRs[ALPHA] self._state[ALPHA] = ( np.round(self._simulation_state[ALPHA] / alpha_increment) * alpha_increment ) @@ -88,10 +89,16 @@ def _update_state(self, a): self._state[THETA_DOT] = self._simulation_state[THETA_DOT] self._state[ALPHA_DOT] = self._simulation_state[ALPHA_DOT] - def reset(self): + def reset( + self, + seed: Optional[int] = None, + options: Optional[dict] = None, + ): + super().reset(seed=seed) self._init_state() - obs, _, _, _ = self.step(np.array([0.0])) - return obs + obs = self.get_obs() + self._init_vel_filt() + return obs, {} class Parameterized(gym.Wrapper): @@ -103,107 +110,3 @@ def params(self): def reset(self, params): self.unwrapped.dyn.params = params return self.env.reset() - - -class QubeDynamics: - """Solve equation M qdd + C(q, qd) = tau for qdd.""" - - def __init__(self): - # Gravity - self.g = 9.81 - - # Motor - self.Rm = 8.4 # resistance (rated voltage/stall current) - self.V = 12.0 # nominal voltage - - # back-emf constant (V-s/rad) - self.km = 0.042 # (rated voltage / no load speed) - - # Rotary arm - self.Mr = 0.095 # mass (kg) - self.Lr = 0.085 # length (m) - self.Dr = 5e-6 # viscous damping (N-m-s/rad), original: 0.0015 - - # Pendulum link - self.Mp = 0.024 # mass (kg) - self.Lp = 0.129 # length (m) - self.Dp = 1e-6 # viscous damping (N-m-s/rad), original: 0.0005 - - # Init constants - self._init_const() - - def _init_const(self): - # Moments of inertia - Jr = self.Mr * self.Lr**2 / 12 # inertia about COM (kg-m^2) - Jp = self.Mp * self.Lp**2 / 12 # inertia about COM (kg-m^2) - - # Constants for equations of motion - self._c = np.zeros(5) - self._c[0] = Jr + self.Mp * self.Lr**2 - self._c[1] = 0.25 * self.Mp * self.Lp**2 - self._c[2] = 0.5 * self.Mp * self.Lp * self.Lr - self._c[3] = Jp + self._c[1] - self._c[4] = 0.5 * self.Mp * self.Lp * self.g - - @property - def params(self): - params = self.__dict__.copy() - params.pop("_c") - return params - - @params.setter - def params(self, params): - self.__dict__.update(params) - self._init_const() - - @profile - def __call__(self, state, action): - # """ - # action between 0 and 1, maps to +V and -V - # """ - th, al, thd, ald = state - voltage = action * self.V - - # Precompute some values - sin_al = sin(al) - sin_2al = sin(2 * al) - cos_al = cos(al) - - # Define mass matrix M = [[a, b], [b, c]] - a = self._c[0] + self._c[1] * sin_al**2 - b = self._c[2] * cos_al - c = self._c[3] - d = a * c - b * b - - # Calculate vector [x, y] = tau - C(q, qd) - trq = self.km * (voltage - self.km * thd) / self.Rm - c0 = self._c[1] * sin_2al * thd * ald - self._c[2] * sin_al * ald * ald - c1 = -0.5 * self._c[1] * sin_2al * thd * thd + self._c[4] * sin_al - x = trq - self.Dr * thd - c0 - y = -self.Dp * ald - c1 - - # Compute M^{-1} @ [x, y] - thdd = (c * x - b * y) / d - aldd = (a * y - b * x) / d - - # chat gpt optimized code lol - # # Define mass matrix M = [[a, b], [b, c]] - # a = self._c[0] + self._c[1] * np.sin(al) ** 2 - # b = self._c[2] * np.cos(al) - # c = self._c[3] - # M = np.array([[a, b], [b, c]]) - # Minv = inv(M) - - # # Calculate vector [x, y] = tau - C(q, qd) - # trq = self.km * (voltage - self.km * thd) / self.Rm - # c0 = self._c[1] * np.sin(2 * al) * thd * ald - self._c[2] * np.sin(al) * ald * ald - # c1 = -0.5 * self._c[1] * np.sin(2 * al) * thd * thd + self._c[4] * np.sin(al) - # x = trq - self.Dr * thd - c0 - # y = -self.Dp * ald - c1 - # v = np.array([x, y]) - - # # Compute M^{-1} @ v - # acc = np.dot(Minv, v) - # thdd, aldd = acc - - return thdd, aldd diff --git a/furuta/rl/utils.py b/furuta/rl/utils.py new file mode 100644 index 0000000..f9ae642 --- /dev/null +++ b/furuta/rl/utils.py @@ -0,0 +1,48 @@ +import logging +import os +import random +from pathlib import Path + +import numpy as np +import torch +import wandb + + +def seed_everything(env, seed, cudnn_deterministic): + # got this from cleanrl + env.action_space.seed(seed) + env.observation_space.seed(seed) + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.backends.cudnn.deterministic = cudnn_deterministic + env.reset(seed=seed) + + +def download_artifact_file(artifact_alias, filename): + """Download artifact and returns path to filename. + + :param artifact_name: wandb artifact alias + :param filename: filename in the artifact + """ + logging.info(f"loading {filename} from {artifact_alias}") + + artifact = wandb.use_artifact(artifact_alias) + artifact_dir = Path(artifact.download()) + filepath = artifact_dir / filename + + assert filepath.is_file(), f"{artifact_alias} doesn't contain {filename}" + + return filepath + + +def upload_file_to_artifacts(pth, artifact_name, artifact_type): + logging.info(f"Saving {pth} to {artifact_name}") + if not isinstance(pth, Path): + pth = Path(pth) + + assert os.path.isfile(pth), f"{pth} is not a file" + + artifact = wandb.Artifact(artifact_name, type=artifact_type) + artifact.add_file(pth) + wandb.log_artifact(artifact) diff --git a/furuta/rl/wrappers.py b/furuta/rl/wrappers.py index 7d9003f..5326b23 100644 --- a/furuta/rl/wrappers.py +++ b/furuta/rl/wrappers.py @@ -1,66 +1,83 @@ import logging import time from pathlib import Path -from typing import Union +from typing import Optional, Union -import gym +import gymnasium as gym +import hydra import numpy as np import wandb -from gym.spaces import Box +from gymnasium.spaces import Box from mcap_protobuf.writer import Writer from furuta.logging.protobuf.pendulum_state_pb2 import PendulumState +from furuta.utils import ALPHA, ALPHA_DOT, THETA, THETA_DOT class GentlyTerminating(gym.Wrapper): """This env wrapper sends zero command to the robot when an episode is done.""" def step(self, action): - observation, reward, done, info = self.env.step(action) - if done: - logging.debug("episode done, killing motor.") - self.env.motor.set_speed(0) - return observation, reward, done, info - - def reset(self): + observation, reward, terminated, truncated, info = self.env.step(action) + if terminated or truncated: + logging.info("episode done, killing motor.") + self.unwrapped.robot.step(0.0) + return observation, reward, terminated, truncated, info + + def reset( + self, + seed: Optional[int] = None, + options: Optional[dict] = None, + ): return self.env.reset() class MCAPLogger(gym.Wrapper): - def __init__(self, env: gym.Env, log_dir: Union[str, Path], use_sim_time: bool): + def __init__(self, env: gym.Env, use_sim_time: bool, log_dir: Union[str, Path] = None): super().__init__(env) - if isinstance(log_dir, str): - log_dir = Path(log_dir) - self.log_dir = log_dir + if log_dir: + if isinstance(log_dir, str): + log_dir = Path(log_dir) + self.log_dir = log_dir + else: + self.log_dir = Path(hydra.core.hydra_config.HydraConfig.get().runtime.output_dir) self.use_sim_time = use_sim_time self.episodes = 0 self.mcap_writer = None def step(self, action): - observation, reward, done, info = self.env.step(action) + observation, reward, terminated, truncated, info = self.env.step(action) if self.use_sim_time: self.sim_time += self.unwrapped.timing.dt if self.use_sim_time: time_to_log = self.sim_time else: - time_to_log = time.time_ns() # TODO check that's the right clock - - # convert to milliseconds - time_to_log = round(time_to_log * 1e9) + time_to_log = time.time_ns() self.mcap_writer.write_message( topic="/pendulum_state", - message=PendulumState(**info), + message=PendulumState( + motor_angle=self.unwrapped._state[THETA], + pendulum_angle=self.unwrapped._state[ALPHA], + motor_angle_velocity=self.unwrapped._state[THETA_DOT], + pendulum_angle_velocity=self.unwrapped._state[ALPHA_DOT], + reward=reward, + action=float(action[0]), + ), log_time=time_to_log, publish_time=time_to_log, ) - return observation, reward, done, info + return observation, reward, terminated, truncated, info - def reset(self): + def reset( + self, + seed: Optional[int] = None, + options: Optional[dict] = None, + ): # create log dir if doesn't exist if not self.log_dir.exists(): self.log_dir.mkdir(parents=True) @@ -73,8 +90,9 @@ def reset(self): self.output_file = open(self.log_dir / fname, "wb") self.mcap_writer = Writer(self.output_file) - # TODO add metadata + # TODO add metadata? # date, control frequency, wandb run id, sim parameters, robot parameters, etc. + # or maybe we should log this to wandb? or maybe just use it for quick local debug? self.episodes += 1 @@ -95,32 +113,45 @@ def close_mcap_writer(self): class ControlFrequency(gym.Wrapper): """Enforce a sleeping time (dt) between each step.""" - def __init__(self, env, dt): + def __init__(self, env, log_freq=3000): super().__init__(env) - self.dt = dt + self.dt = env.unwrapped.timing.dt + self.last = None + + # TODO make control freq reporting better + # or maybe not, maybe we should bench speed in designated script see #55 + # average control freq over many steps + self.log_freq = log_freq + self.nb_steps = 0 def step(self, action): + self.nb_steps += 1 current = time.time() - _, _, _, _ = self.env.step(action) loop_time = 0 if self.last is not None: loop_time = current - self.last sleeping_time = self.dt - loop_time + if self.nb_steps % self.log_freq == 0: + logging.info(f"control freq: {1/loop_time}") if sleeping_time > 0: time.sleep(sleeping_time) else: - print("warning, loop time > dt") + logging.info("warning, loop time > dt") - obs, reward, done, info = self.env.step(np.array([0.0])) + obs, reward, terminated, truncated, info = self.env.step(action) self.last = time.time() if logging.root.level == logging.DEBUG: wandb.log({**info, **{"loop time": loop_time}}) - return obs, reward, done, info + return obs, reward, terminated, truncated, info - def reset(self): + def reset( + self, + seed: Optional[int] = None, + options: Optional[dict] = None, + ): self.last = None return self.env.reset() @@ -159,7 +190,7 @@ def _continuity_cost(self, obs): return continuity_cost def step(self, action): - obs, reward, done, info = self.env.step(action) + obs, reward, terminated, truncated, info = self.env.step(action) self.history.pop(0) obs = np.concatenate([obs, action]) @@ -171,11 +202,15 @@ def step(self, action): reward -= continuity_cost info["continuity_cost"] = continuity_cost - return obs, reward, done, info + return obs, reward, terminated, truncated, info - def reset(self): + def reset( + self, + seed: Optional[int] = None, + options: Optional[dict] = None, + ): self.history = self._make_history() self.history.pop(0) - obs = np.concatenate([self.env.reset(), np.zeros_like(self.env.action_space.low)]) + obs = np.concatenate([self.env.reset()[0], np.zeros_like(self.env.action_space.low)]) self.history.append(obs) - return np.array(self.history) + return np.array(self.history), {} diff --git a/furuta/robot.py b/furuta/robot.py index eaa1b2d..b63c3db 100644 --- a/furuta/robot.py +++ b/furuta/robot.py @@ -9,7 +9,6 @@ def __init__(self, device="dev/ttyACM0", baudrate=921600): self.ser = serial.Serial(device, baudrate) def step(self, motor_command: float): - """Motor command is a float between -1 and 1.""" direction = motor_command < 0 # convert motor command to 16 bit unsigned int int_motor_command = int(np.abs(motor_command) * (2**16 - 1)) @@ -30,3 +29,100 @@ def reset_encoders(self): def close(self): self.step(0) self.ser.close() + + +class QubeDynamics: + """Solve equation M qdd + C(q, qd) = tau for qdd.""" + + def __init__( + self, + g=9.81, + # Motor + Rm=8.4, # reskstance (rated voltage/stall current) + V=12.0, # nominal voltage + km=0.042, # back-emf constant (V-s/rad) = (rated voltage / no load speed) + # Rotary Arm + Mr=0.095, # mass (kg) + Lr=0.085, # length (m) + Dr=5e-6, # viscous damping (N-m-s/rad), original: 0.0015 + # Pendulum Link + Mp=0.024, # mass (kg) + Lp=0.129, # length (m) + Dp=1e-6, # viscous damping (N-m-s/rad), original: 0.0005 + ): + # Gravity + self.g = g + + # Motor + self.Rm = Rm + self.V = V + + self.km = km + + # Rotary arm + self.Mr = Mr + self.Lr = Lr + self.Dr = Dr + + # Pendulum link + self.Mp = Mp + self.Lp = Lp + self.Dp = Dp + + # Init constants + self._init_const() + + def _init_const(self): + # Moments of inertia + Jr = self.Mr * self.Lr**2 / 12 # inertia about COM (kg-m^2) + Jp = self.Mp * self.Lp**2 / 12 # inertia about COM (kg-m^2) + + # Constants for equations of motion + self._c = np.zeros(5) + self._c[0] = Jr + self.Mp * self.Lr**2 + self._c[1] = 0.25 * self.Mp * self.Lp**2 + self._c[2] = 0.5 * self.Mp * self.Lp * self.Lr + self._c[3] = Jp + self._c[1] + self._c[4] = 0.5 * self.Mp * self.Lp * self.g + + @property + def params(self): + params = self.__dict__.copy() + params.pop("_c") + return params + + @params.setter + def params(self, params): + self.__dict__.update(params) + self._init_const() + + def __call__(self, state, action): + # """ + # action between 0 and 1, maps to +V and -V + # """ + th, al, thd, ald = state + voltage = action * self.V + + # Precompute some values + sin_al = np.sin(al) + sin_2al = np.sin(2 * al) + cos_al = np.cos(al) + + # Define mass matrix M = [[a, b], [b, c]] + a = self._c[0] + self._c[1] * sin_al**2 + b = self._c[2] * cos_al + c = self._c[3] + d = a * c - b * b + + # Calculate vector [x, y] = tau - C(q, qd) + trq = self.km * (voltage - self.km * thd) / self.Rm + c0 = self._c[1] * sin_2al * thd * ald - self._c[2] * sin_al * ald * ald + c1 = -0.5 * self._c[1] * sin_2al * thd * thd + self._c[4] * sin_al + x = trq - self.Dr * thd - c0 + y = -self.Dp * ald - c1 + + # Compute M^{-1} @ [x, y] + thdd = (c * x - b * y) / d + aldd = (a * y - b * x) / d + + return thdd, aldd diff --git a/furuta/utils.py b/furuta/utils.py index bd535b2..c1206ae 100644 --- a/furuta/utils.py +++ b/furuta/utils.py @@ -1,6 +1,6 @@ # https://git.ias.informatik.tu-darmstadt.de/quanser/clients/-/blob/v0.1.1/quanser_robots/common.py import numpy as np -from gym import spaces +from gymnasium import spaces from scipy import signal THETA = 0 diff --git a/poetry.lock b/poetry.lock index 5404f2e..2a90370 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,5 +1,40 @@ # This file is automatically @generated by Poetry 1.4.0 and should not be changed by hand. +[[package]] +name = "absl-py" +version = "2.0.0" +description = "Abseil Python Common Libraries, see https://github.com/abseil/abseil-py." +category = "main" +optional = false +python-versions = ">=3.7" +files = [ + {file = "absl-py-2.0.0.tar.gz", hash = "sha256:d9690211c5fcfefcdd1a45470ac2b5c5acd45241c3af71eed96bc5441746c0d5"}, + {file = "absl_py-2.0.0-py3-none-any.whl", hash = "sha256:9a28abb62774ae4e8edbe2dd4c49ffcd45a6a848952a5eccc6a49f3f0fc1e2f3"}, +] + +[[package]] +name = "antlr4-python3-runtime" +version = "4.9.3" +description = "ANTLR 4.9.3 runtime for Python 3.7" +category = "main" +optional = false +python-versions = "*" +files = [ + {file = "antlr4-python3-runtime-4.9.3.tar.gz", hash = "sha256:f224469b4168294902bb1efa80a8bf7855f24c99aef99cbefc1bcd3cce77881b"}, +] + +[[package]] +name = "appdirs" +version = "1.4.4" +description = "A small Python module for determining appropriate platform-specific dirs, e.g. a \"user data dir\"." +category = "main" +optional = false +python-versions = "*" +files = [ + {file = "appdirs-1.4.4-py2.py3-none-any.whl", hash = "sha256:a841dacd6b99318a741b166adb07e19ee71a274450e68237b4650ca1055ab128"}, + {file = "appdirs-1.4.4.tar.gz", hash = "sha256:7d5d0167b2b1ba821647616af46a749d1c653740dd0d2415100fe26e27afdf41"}, +] + [[package]] name = "appnope" version = "0.1.3" @@ -43,6 +78,30 @@ files = [ {file = "backcall-0.2.0.tar.gz", hash = "sha256:5cbdbf27be5e7cfadb448baf0aa95508f91f2bbc6c6437cd9cd06e2a4c215e1e"}, ] +[[package]] +name = "cachetools" +version = "5.3.2" +description = "Extensible memoizing collections and decorators" +category = "main" +optional = false +python-versions = ">=3.7" +files = [ + {file = "cachetools-5.3.2-py3-none-any.whl", hash = "sha256:861f35a13a451f94e301ce2bec7cac63e881232ccce7ed67fab9b5df4d3beaa1"}, + {file = "cachetools-5.3.2.tar.gz", hash = "sha256:086ee420196f7b2ab9ca2db2520aca326318b68fe5ba8bc4d49cca91add450f2"}, +] + +[[package]] +name = "certifi" +version = "2023.11.17" +description = "Python package for providing Mozilla's CA Bundle." +category = "main" +optional = false +python-versions = ">=3.6" +files = [ + {file = "certifi-2023.11.17-py3-none-any.whl", hash = "sha256:e036ab49d5b79556f99cfc2d9320b34cfbe5be05c5871b51de9329f0603b0474"}, + {file = "certifi-2023.11.17.tar.gz", hash = "sha256:9b469f3a900bf28dc19b8cfbf8019bf47f7fdd1a65a1d4ffb98fc14166beb4d1"}, +] + [[package]] name = "cffi" version = "1.16.0" @@ -120,6 +179,121 @@ files = [ {file = "cfgv-3.4.0.tar.gz", hash = "sha256:e52591d4c5f5dead8e0f673fb16db7949d2cfb3f7da4582893288f0ded8fe560"}, ] +[[package]] +name = "charset-normalizer" +version = "3.3.2" +description = "The Real First Universal Charset Detector. 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">=3.9,<4.0" gymnasium = "^0.29.1" pyserial = "^3.5" simple-pid = "^2.0.0" ipykernel = "^6.26.0" matplotlib = "3.7.3" pre-commit = "^3.5.0" +stable-baselines3 = "^2.2.1" +wandb = "^0.16.2" +mcap-protobuf-support = "^0.4.1" +scipy = "^1.11.4" +pygame = "^2.5.2" +opencv-python = "^4.9.0.80" +hydra-core = "^1.3.2" +tensorboard = "^2.15.1" +moviepy = "^1.0.3" [tool.poetry.group.dev.dependencies] diff --git a/scripts/configs/algo/ppo.yaml b/scripts/configs/algo/ppo.yaml new file mode 100644 index 0000000..27019b1 --- /dev/null +++ b/scripts/configs/algo/ppo.yaml @@ -0,0 +1,4 @@ +_target_: stable_baselines3.PPO +policy: "MlpPolicy" +use_sde: True +sde_sample_freq: 64 diff --git a/scripts/configs/algo/sac.yaml b/scripts/configs/algo/sac.yaml index 41faacf..1709d95 100644 --- a/scripts/configs/algo/sac.yaml +++ b/scripts/configs/algo/sac.yaml @@ -1,5 +1,6 @@ # refer to https://stable-baselines3.readthedocs.io/en/master/modules/sac.html -_target_: furuta_gym.algos.SAC +_target_: furuta.rl.algos.SAC +# _target_: stable_baselines3.SAC policy: "MlpPolicy" learning_rate: 0.0003 buffer_size: 1_000_000 @@ -11,5 +12,5 @@ learning_starts: 500 use_sde: True use_sde_at_warmup: True sde_sample_freq: 64 -train_freq: [1, "episode"] +train_freq: 1 # 1 step gradient_steps: -1 diff --git a/scripts/configs/algo/sac_real.yaml b/scripts/configs/algo/sac_real.yaml new file mode 100644 index 0000000..2d3067b --- /dev/null +++ b/scripts/configs/algo/sac_real.yaml @@ -0,0 +1,6 @@ +defaults: + - sac + +learning_starts: 1 +sde_sample_freq: 32 +train_freq: [1, "episode"] diff --git a/scripts/configs/env/dyn/default.yaml b/scripts/configs/env/dyn/default.yaml new file mode 100644 index 0000000..6a651dd --- /dev/null +++ b/scripts/configs/env/dyn/default.yaml @@ -0,0 +1 @@ +_target_: furuta.robot.QubeDynamics diff --git a/scripts/configs/env/dyn/tiny.yaml b/scripts/configs/env/dyn/tiny.yaml new file mode 100644 index 0000000..027de44 --- /dev/null +++ b/scripts/configs/env/dyn/tiny.yaml @@ -0,0 +1,2 @@ +_target_: furuta.robot.QubeDynamics +# use this file to store the sim params for the tiny version of the robot diff --git a/scripts/configs/env/real.yaml b/scripts/configs/env/real.yaml new file mode 100644 index 0000000..a35001b --- /dev/null +++ b/scripts/configs/env/real.yaml @@ -0,0 +1,6 @@ +_target_: furuta.rl.envs.furuta_real.FurutaReal +control_freq: 50 +reward: "alpha" +angle_limits: [3.14, 6.3] +speed_limits: null # [60, 400] +usb_device: "/dev/ttyACM0" diff --git a/scripts/configs/env/sim.yaml b/scripts/configs/env/sim.yaml new file mode 100644 index 0000000..77f6cb1 --- /dev/null +++ b/scripts/configs/env/sim.yaml @@ -0,0 +1,12 @@ +defaults: + - _self_ + - dyn: default + +_target_: furuta.rl.envs.furuta_sim.FurutaSim +control_freq: 50 +reward: "alpha" +angle_limits: [3.14, 12] +speed_limits: [60, 400] +encoders_CPRs: null +velocity_filter: 2 +render_mode: "rgb_array" diff --git a/scripts/configs/experiment/real_finetune.yaml b/scripts/configs/experiment/real_finetune.yaml new file mode 100644 index 0000000..36b356b --- /dev/null +++ b/scripts/configs/experiment/real_finetune.yaml @@ -0,0 +1,10 @@ +# @package _global_ +defaults: + - override /env: real + - override /wrappers: real_wrappers + - override /algo: sac_real + +total_timesteps: 150_000 +n_envs: 1 +model_artifact: "model:v7" +capture_video: False diff --git a/scripts/configs/experiment/sim.yaml b/scripts/configs/experiment/sim.yaml index 84fb15e..5cc9093 100644 --- a/scripts/configs/experiment/sim.yaml +++ b/scripts/configs/experiment/sim.yaml @@ -4,4 +4,4 @@ defaults: - override /wrappers: base_wrappers gym_id: "FurutaSim-v0" -total_timesteps: 50_000 +total_timesteps: 150_000 diff --git a/scripts/configs/rl_inference.yaml b/scripts/configs/rl_inference.yaml new file mode 100644 index 0000000..c8b4559 --- /dev/null +++ b/scripts/configs/rl_inference.yaml @@ -0,0 +1,8 @@ +defaults: + - _self_ + - env: real + - wrappers: real_wrappers + +model_artifact: "model:latest" +render: False +nb_episodes: 1 diff --git a/scripts/configs/sweeps/bounds.yaml b/scripts/configs/sweeps/bounds.yaml new file mode 100644 index 0000000..fc6670d --- /dev/null +++ b/scripts/configs/sweeps/bounds.yaml @@ -0,0 +1,16 @@ +program: train.py +method: grid +parameters: + env.angle_limits: + values: [null, [3.14, 6.3], [3.14, 400], [400, 6.3], [12, 6.3]] + env.speed_limits: + values: [null, [30, 400], [60, 400], [120, 400]] + seed: + values: [1, 2, 3] + +command: + - ${env} + - python + - ${program} + - ${args_no_hyphens} + - +experiment=sim diff --git a/scripts/configs/sweeps/encoders_simulation.yaml b/scripts/configs/sweeps/encoders_simulation.yaml index a6427e6..a71d70a 100644 --- a/scripts/configs/sweeps/encoders_simulation.yaml +++ b/scripts/configs/sweeps/encoders_simulation.yaml @@ -1,10 +1,8 @@ program: train.py method: grid parameters: - env: - values: ["sim", "sim_encoders", "sim_precise_encoders"] - env.velocity_filter: - values: [null, 2, 3, 4, 10] + env.encoders_CPRs: + values: [[100, 4000.0], [211.2, 8192.0], [400.0, 16000.0]] seed: values: [1, 2, 3, 4] diff --git a/scripts/configs/train.yaml b/scripts/configs/train.yaml index 46822e3..d28df52 100644 --- a/scripts/configs/train.yaml +++ b/scripts/configs/train.yaml @@ -6,15 +6,15 @@ defaults: - wrappers: ??? - algo: sac.yaml -gym_id: ??? -total_timesteps: 1_000_000 +total_timesteps: 500_000 +n_envs: 12 # eval & early stopping evaluation: early_stopping_reward_threshold: 800 # use null for no early stopping deterministic: True n_eval_episodes: 10 - eval_freq: 500 + eval_freq: 50_000 # reproducibility seed: 1 @@ -25,13 +25,12 @@ debug: True capture_video: True wandb: entity: null - project: furuta + project: furuta2 # resume training: load replay buffer and/or model replay_buffer_artifact: null model_artifact: null # logging -save_model: False +save_model: True save_replay_buffer: False -# add mcap diff --git a/scripts/configs/wrappers/base_wrappers.yaml b/scripts/configs/wrappers/base_wrappers.yaml index 3bcc168..747d90d 100644 --- a/scripts/configs/wrappers/base_wrappers.yaml +++ b/scripts/configs/wrappers/base_wrappers.yaml @@ -1,14 +1,6 @@ -# slightly unsure if wrappers will be applied in the order they are listed here -# looks like the order is preserved but unsure if there could be edge cases? -# the order of the wrappers is important -TimeLimit: - module: "gym.wrappers" +- _target_: gymnasium.wrappers.TimeLimit max_episode_steps: 1000 - -HistoryWrapper: - module: "furuta_gym.wrappers" +- _target_: furuta.rl.wrappers.HistoryWrapper steps: 2 use_continuity_cost: True - -Monitor: - module: "stable_baselines3.common.monitor" +- _target_: stable_baselines3.common.monitor.Monitor diff --git a/scripts/configs/wrappers/real_wrappers.yaml b/scripts/configs/wrappers/real_wrappers.yaml index 0179562..8f23307 100644 --- a/scripts/configs/wrappers/real_wrappers.yaml +++ b/scripts/configs/wrappers/real_wrappers.yaml @@ -1,4 +1,10 @@ -# import common wrappers between sim/real -defaults: - - base_wrappers -# add wrappers specific to the robot +- _target_: gymnasium.wrappers.TimeLimit + max_episode_steps: 1000 +- _target_: furuta.rl.wrappers.HistoryWrapper + steps: 2 + use_continuity_cost: True +- _target_: stable_baselines3.common.monitor.Monitor +- _target_: furuta.rl.wrappers.GentlyTerminating +- _target_: furuta.rl.wrappers.ControlFrequency +# - _target_: furuta.rl.wrappers.MCAPLogger +# use_sim_time: False diff --git a/scripts/train.py b/scripts/train.py index b1590ae..5e97ce0 100644 --- a/scripts/train.py +++ b/scripts/train.py @@ -1,75 +1,30 @@ import copy -import importlib import logging -import os -import random -from pathlib import Path -import gym import hydra -import numpy as np -import stable_baselines3 -import torch -import wandb from omegaconf import DictConfig, OmegaConf, open_dict from stable_baselines3.common.callbacks import ( EvalCallback, StopTrainingOnRewardThreshold, ) -from stable_baselines3.common.vec_env import DummyVecEnv, VecVideoRecorder - -import furuta # noqa F420 - -# TODO -# - save model/replay buffer X -# - finish setup wrappers: think about how to setup; maybe have some hardcoded wrappers? -# - load custom sim parameters X - - -def download_artifact_file(artifact_alias, filename): - """Download artifact and returns path to filename. - - :param artifact_name: wandb artifact alias - :param filename: filename in the artifact - """ - logging.info(f"loading {filename} from {artifact_alias}") - - artifact = wandb.use_artifact(artifact_alias) - artifact_dir = Path(artifact.download()) - filepath = artifact_dir / filename - - assert filepath.is_file(), f"{artifact_alias} doesn't contain {filename}" - - return filepath - - -def upload_file_to_artifacts(pth, artifact_name, artifact_type): - logging.info(f"Saving {pth} to {artifact_name}") - if not isinstance(pth, Path): - pth = Path(pth) - - assert os.path.isfile(pth), f"{pth} is not a file" - - artifact = wandb.Artifact(artifact_name, type=artifact_type) - artifact.add_file(pth) - wandb.log_artifact(artifact) - - -def instantiate_gym_wrapper(wrapper_name: str, wrapper_args: DictConfig, env: gym.Env) -> gym.Env: - """Instantiate a gym wrapper from a config.""" - module = importlib.import_module(wrapper_args.module) - wrapper_class = getattr(module, wrapper_name) - - # pop module from DictConfig wrappers args - with open_dict(wrapper_args): - wrapper_args.pop("module") +from stable_baselines3.common.env_checker import check_env +from stable_baselines3.common.vec_env import ( + DummyVecEnv, + SubprocVecEnv, + VecVideoRecorder, +) - return wrapper_class(env, **wrapper_args) +import wandb +from furuta.rl.envs.furuta_real import FurutaReal +from furuta.rl.utils import ( + download_artifact_file, + seed_everything, + upload_file_to_artifacts, +) @hydra.main(version_base="1.3", config_path="configs", config_name="train.yaml") def main(cfg: DictConfig): - print(cfg) logging_level = logging.DEBUG if cfg.debug else logging.INFO logging.basicConfig(format="%(levelname)s: %(message)s", level=logging_level) @@ -85,39 +40,36 @@ def main(cfg: DictConfig): ) # setup env - env = gym.make(cfg.gym_id, **cfg.env) + env = hydra.utils.instantiate(cfg.env, _recursive_=True) + check_env(env) # seed everything - env.seed(cfg.seed) - env.action_space.seed(cfg.seed) - env.observation_space.seed(cfg.seed) - random.seed(cfg.seed) - np.random.seed(cfg.seed) - torch.manual_seed(cfg.seed) - torch.backends.cudnn.deterministic = cfg.cudnn_deterministic + seed_everything(env, cfg.seed, cfg.cudnn_deterministic) # setup wrappers - for wrapper_name, wrapper_args in cfg.wrappers.items(): - try: - env = instantiate_gym_wrapper(wrapper_name, wrapper_args, env) - except Exception as e: - # print stack trace - logging.warning(e, exc_info=True) + for wrapper in cfg.wrappers: + env = hydra.utils.instantiate(wrapper, env=env) + + # don't paralelize if it's the real robot + if isinstance(env.unwrapped, FurutaReal): + vec_env = DummyVecEnv([lambda: env]) + if cfg.n_envs > 1: + logging.warning("n_envs > 1 but using real robot, ignoring n_envs") + else: + vec_env = SubprocVecEnv([lambda: copy.deepcopy(env) for _ in range(cfg.n_envs)]) # setup algo/model verbose = 2 if cfg.debug else 0 model = hydra.utils.instantiate( - cfg.algo, env=env, tensorboard_log=f"runs/{run.id}", verbose=verbose + cfg.algo, env=vec_env, tensorboard_log=f"runs/{run.id}", verbose=verbose, _convert_="all" ) # load model/replay buffer if cfg.model_artifact: - model.load(download_artifact_file(f"model:{cfg.model_artifact}", "model.zip")) + model.load(download_artifact_file(cfg.model_artifact, "model.zip")) if cfg.replay_buffer_artifact: - rb_path = download_artifact_file( - f"replay_buffer:{cfg.replay_buffer_artifact}", "buffer.pkl" - ) + rb_path = download_artifact_file(cfg.replay_buffer_artifact, "buffer.pkl") model.load_replay_buffer(rb_path) # Stop training when the model reaches the reward threshold @@ -132,11 +84,14 @@ def main(cfg: DictConfig): # use same env for eval # TODO maybe do eval even when we don't want to early stop? # would it be useful? + # accounting for vec envs + + eval_freq = max(cfg.evaluation.eval_freq // cfg.n_envs, 1) eval_callback = EvalCallback( eval_env, deterministic=cfg.evaluation.deterministic, n_eval_episodes=cfg.evaluation.n_eval_episodes, - eval_freq=cfg.evaluation.eval_freq, + eval_freq=eval_freq, callback_on_new_best=callback_on_best, verbose=1, ) @@ -149,9 +104,6 @@ def main(cfg: DictConfig): # only save last video if cfg.capture_video: - # TODO add headleas arg, depends on the machine - # import pyvirtualdisplay - # pyvirtualdisplay.Display(visible=0, size=(1400, 900)).start() video_length = 500 env = DummyVecEnv([lambda: env]) env = VecVideoRecorder( @@ -172,15 +124,15 @@ def main(cfg: DictConfig): if cfg.save_model: logging.info("Saving model to artifacts") - model_path = f"runs/{run.id}/models/sac.zip" + model_path = f"runs/{run.id}/models/model.zip" model.save(model_path) - upload_file_to_artifacts(model_path, "sac_model", "model") + upload_file_to_artifacts(model_path, "model", "model") if cfg.save_replay_buffer: logging.info("Saving replay_buffer to artifacts") buffer_path = f"runs/{run.id}/buffers/buffer.pkl" model.save_replay_buffer(buffer_path) - upload_file_to_artifacts(buffer_path, "sac_replay_buffer", "replay buffer") + upload_file_to_artifacts(buffer_path, "replay_buffer", "replay buffer") env.close() run.finish() diff --git a/tests/test_gym_env.py b/tests/test_gym_env.py new file mode 100644 index 0000000..2154e18 --- /dev/null +++ b/tests/test_gym_env.py @@ -0,0 +1,13 @@ +from stable_baselines3.common.env_checker import check_env + +from furuta.rl.envs.furuta_real import FurutaReal +from furuta.rl.envs.furuta_sim import FurutaSim + + +def test_sim_env(): + env = FurutaSim() + check_env(env) + + +# TODO how to check real env? +# would need to mock the robot