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train.py
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train.py
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import argparse
import time
import random
import numpy as np
import torch
from torch.autograd import Variable
from gym.spaces import Box, Dict, Discrete, MultiBinary, MultiDiscrete
from marl_algorithms.maddpg.maddpg import MADDPG
from marl_algorithms.iql.iql import IQL
from marl_algorithms.buffer import ReplayBuffer
from utilities.model_saver import ModelSaver
from utilities.logger import Logger
from utilities.plotter import Plotter
from utilities.frame_saver import FrameSaver
USE_CUDA = torch.cuda.is_available()
GOAL_EPSILON = 0.01
class Train:
def __init__(self):
self.parser = argparse.ArgumentParser(
"Reinforcement Learning experiments for multiagent environments"
)
self.parse_args()
self.arglist = self.parser.parse_args()
def parse_default_args(self):
"""
Parse default arguments for MARL training script
"""
# algorithm
self.parser.add_argument(
"--alg", type=str, default="maddpg", help="name of the algorithm to use"
)
self.parser.add_argument("--hidden_dim", default=128, type=int)
# curiosity
self.parser.add_argument(
"--curiosity", type=str, default=None, help="name of curiosity to use"
)
self.parser.add_argument(
"--joint_curiosity",
action="store_true",
default=False,
help="flag if curiosity should be applied jointly for all agents",
)
self.parser.add_argument(
"--curiosity_hidden_dim",
type=int,
default=64,
help="curiosity internal state representation size",
)
self.parser.add_argument(
"--curiosity_state_rep_size",
type=int,
default=64,
help="curiosity internal state representation size",
)
self.parser.add_argument(
"--count_key_dim",
type=int,
default=32,
help="key dimensionality of hash-count-based curiosity",
)
self.parser.add_argument(
"--count_decay", type=float, default=1, help="factor for count decay speed"
)
self.parser.add_argument(
"--eta", type=int, default=5, help="curiosity loss weighting factor"
)
self.parser.add_argument(
"--curiosity_lr",
type=float,
default=5e-6,
help="learning rate for curiosity optimizer",
)
# training length
self.parser.add_argument(
"--num_episodes", type=int, default=25000, help="number of episodes"
)
self.parser.add_argument(
"--max_episode_len", type=int, default=25, help="maximum episode length"
)
# core training parameters
self.parser.add_argument(
"--n_training_threads", default=6, type=int, help="number of training threads"
)
self.parser.add_argument(
"--no_rewards",
action="store_true",
default=False,
help="flag if no rewards should be used",
)
self.parser.add_argument(
"--sparse_rewards",
action="store_true",
default=False,
help="flag if sparse rewards should be used",
)
self.parser.add_argument(
"--sparse_freq", type=int, default=25, help="number of steps before sparse rewards"
)
self.parser.add_argument("--gamma", type=float, default=0.9, help="discount factor")
self.parser.add_argument(
"--tau", type=float, default=0.01, help="tau as stepsize for target network updates"
)
self.parser.add_argument(
"--lr", type=float, default=0.01, help="learning rate for Adam optimizer"
)
self.parser.add_argument(
"--dropout_p", type=float, default=0.0, help="Dropout probability"
)
self.parser.add_argument(
"--seed", type=int, default=None, help="random seed used throughout training"
)
self.parser.add_argument(
"--steps_per_update", type=int, default=100, help="number of steps before updates"
)
self.parser.add_argument(
"--buffer_capacity", type=int, default=int(1e6), help="Replay buffer capacity"
)
self.parser.add_argument(
"--batch_size",
type=int,
default=1024,
help="number of episodes to optimize at the same time",
)
# exploration settings
self.parser.add_argument(
"--no_exploration",
action="store_true",
default=False,
help="flag if no exploration should be used",
)
self.parser.add_argument(
"--decay_factor", type=float, default=0.99999, help="exploration decay factor"
)
self.parser.add_argument(
"--exploration_bonus", type=float, default=1.0, help="exploration bonus value"
)
self.parser.add_argument("--n_exploration_eps", default=25000, type=int)
self.parser.add_argument("--init_noise_scale", default=0.3, type=float)
self.parser.add_argument("--final_noise_scale", default=0.0, type=float)
# visualisation
self.parser.add_argument("--display", action="store_true", default=False)
self.parser.add_argument("--save_frames", action="store_true", default=False)
self.parser.add_argument(
"--plot", action="store_true", default=False, help="plot reward and exploration bonus"
)
self.parser.add_argument(
"--eval_frequency", default=100, type=int, help="frequency of evaluation episodes"
)
self.parser.add_argument(
"--eval_episodes", default=5, type=int, help="number of evaluation episodes"
)
self.parser.add_argument(
"--dump_losses",
action="store_true",
default=False,
help="dump losses after computation",
)
# run name for store path
self.parser.add_argument(
"--run", type=str, default="default", help="run name for stored paths"
)
# model storing
self.parser.add_argument(
"--save_models_dir",
type=str,
default="models",
help="path where models should be saved",
)
self.parser.add_argument("--save_interval", default=1000, type=int)
self.parser.add_argument(
"--load_models",
type=str,
default=None,
help="path where models should be loaded from if set",
)
self.parser.add_argument(
"--load_models_extension",
type=str,
default="final",
help="name extension for models to load",
)
def parse_args(self):
"""
parse own arguments
"""
self.parse_default_args()
def extract_sizes(self, spaces):
"""
Extract space dimensions
:param spaces: list of Gym spaces
:return: list of ints with sizes for each agent
"""
sizes = []
for space in spaces:
if isinstance(space, Box):
size = sum(space.shape)
elif isinstance(space, Dict):
size = sum(self.extract_sizes(space.values()))
elif isinstance(space, Discrete) or isinstance(space, MultiBinary):
size = space.n
elif isinstance(space, MultiDiscrete):
size = sum(space.nvec)
else:
raise ValueError("Unknown class of space: ", type(space))
sizes.append(size)
return sizes
def create_environment(self):
"""
Create environment instance
:return: environment (gym interface), env_name, task_name, n_agents, observation_sizes,
action_sizes, discrete_actions
"""
raise NotImplementedError()
def reset_environment(self):
"""
Reset environment for new episode
:return: observation (as torch tensor)
"""
raise NotImplementedError
def select_actions(self, obs, explore=True):
"""
Select actions for agents
:param obs: joint observation
:param explore: flag if exploration should be used
:return: action_tensor, action_list
"""
raise NotImplementedError()
def environment_step(self, actions):
"""
Take step in the environment
:param actions: actions to apply for each agent
:return: reward, done, next_obs (as Pytorch tensors)
"""
raise NotImplementedError()
def environment_render(self):
"""
Render visualisation of environment
"""
raise NotImplementedError()
def eval(self, ep, n_agents):
"""
Execute evaluation episode without exploration
:param ep: episode number
:param n_agents: number of agents in task
:return: episode_rewards, episode_length, done
"""
obs = self.reset_environment()
self.alg.reset(ep)
episode_rewards = np.array([0.0] * n_agents)
episode_length = 0
done = False
while not done and episode_length < self.arglist.max_episode_len:
torch_obs = [
Variable(torch.Tensor(obs[i]), requires_grad=False) for i in range(n_agents)
]
actions, _ = self.select_actions(torch_obs, False)
rewards, dones, next_obs = self.environment_step(actions)
episode_rewards += rewards
obs = next_obs
episode_length += 1
done = all(dones)
return episode_rewards, episode_length, done
def train(self):
"""
Abstract training flow
"""
# set random seeds before model creation
if self.arglist.seed is not None:
random.seed(self.arglist.seed)
np.random.seed(self.arglist.seed)
torch.manual_seed(self.arglist.seed)
torch.cuda.manual_seed(self.arglist.seed)
if torch.cuda.is_available():
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
# use number of threads if no GPUs are available
if not USE_CUDA:
torch.set_num_threads(self.arglist.n_training_threads)
env, env_name, task_name, n_agents, observation_sizes, action_sizes, discrete_actions = (
self.create_environment()
)
self.env = env
self.n_agents = n_agents
steps = self.arglist.num_episodes * self.arglist.max_episode_len
# steps-th root of GOAL_EPSILON
decay_epsilon = GOAL_EPSILON ** (1 / float(steps))
self.arglist.decay_factor = decay_epsilon
print(
"Epsilon is decaying with factor %.7f to %.3f over %d steps."
% (decay_epsilon, GOAL_EPSILON, steps)
)
print("Observation sizes: ", observation_sizes)
print("Action sizes: ", action_sizes)
# Create curiosity instances
if self.arglist.curiosity is None:
print("No curiosity is to be used!")
elif self.arglist.curiosity == "icm":
print("Training uses Intrinsic Curiosity Module (ICM)!")
elif self.arglist.curiosity == "rnd":
print("Training uses Random Network Distillation (RND)!")
elif self.arglist.curiosity == "count":
print("Training uses hash-based counting exploration bonus!")
else:
raise ValueError("Unknown curiosity: " + self.arglist.curiosity)
# create algorithm trainer
if self.arglist.alg == "maddpg":
self.alg = MADDPG(
n_agents, observation_sizes, action_sizes, discrete_actions, self.arglist
)
print(
"Training multi-agent deep deterministic policy gradient (MADDPG) on "
+ env_name
+ " environment"
)
elif self.arglist.alg == "iql":
self.alg = IQL(
n_agents, observation_sizes, action_sizes, discrete_actions, self.arglist
)
print("Training independent q-learning (IQL) on " + env_name + " environment")
else:
raise ValueError("Unknown algorithm: " + self.arglist.alg)
self.memory = ReplayBuffer(
self.arglist.buffer_capacity,
n_agents,
observation_sizes,
action_sizes,
self.arglist.no_rewards,
)
# set random seeds past model creation
if self.arglist.seed is not None:
random.seed(self.arglist.seed)
np.random.seed(self.arglist.seed)
torch.manual_seed(self.arglist.seed)
torch.cuda.manual_seed(self.arglist.seed)
if torch.cuda.is_available():
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
if self.arglist.load_models is not None:
print(
"Loading models from "
+ self.arglist.load_models
+ " with extension "
+ self.arglist.load_models_extension
)
self.alg.load_model_networks(
self.arglist.load_models, "_" + self.arglist.load_models_extension
)
self.model_saver = ModelSaver(
self.arglist.save_models_dir, self.arglist.run, self.arglist.alg
)
self.logger = Logger(
n_agents,
self.arglist.eta,
task_name,
self.arglist.run,
self.arglist.alg,
self.arglist.curiosity,
)
self.plotter = Plotter(
self.logger,
n_agents,
self.arglist.eval_frequency,
task_name,
self.arglist.run,
self.arglist.alg,
self.arglist.curiosity,
)
if self.arglist.save_frames:
self.frame_saver = FrameSaver(
self.arglist.eta, task_name, self.arglist.run, self.arglist.alg
)
print("Starting iterations...")
start_time = time.time()
t = 0
for ep in range(self.arglist.num_episodes):
obs = self.reset_environment()
self.alg.reset(ep)
episode_rewards = np.array([0.0] * n_agents)
if self.arglist.sparse_rewards:
sparse_rewards = np.array([0.0] * n_agents)
episode_length = 0
done = False
interesting_episode = False
while not done and episode_length < self.arglist.max_episode_len:
torch_obs = [
Variable(torch.Tensor(obs[i]), requires_grad=False) for i in range(n_agents)
]
actions, agent_actions = self.select_actions(
torch_obs, not self.arglist.no_exploration
)
rewards, dones, next_obs = self.environment_step(actions)
episode_rewards += rewards
if self.arglist.sparse_rewards:
sparse_rewards += rewards
if self.arglist.no_rewards:
rewards = [0.0] * n_agents
elif self.arglist.sparse_rewards:
if (episode_length + 1) % self.arglist.sparse_freq == 0:
rewards = list(sparse_rewards / self.arglist.sparse_freq)
else:
rewards = [0.0] * n_agents
self.memory.push(obs, agent_actions, rewards, next_obs, dones)
t += 1
if (
len(self.memory) >= self.arglist.batch_size
and (t % self.arglist.steps_per_update) == 0
):
losses = self.alg.update(self.memory, USE_CUDA)
self.logger.log_losses(ep, losses)
if self.arglist.dump_losses:
self.logger.dump_losses(1)
# for displaying learned policies
if self.arglist.display:
self.environment_render()
if self.arglist.save_frames:
self.frame_saver.add_frame(self.env.render("rgb_array")[0], ep)
if self.arglist.curiosity is not None:
curiosities = self.alg.get_curiosities(obs, agent_actions, next_obs)
interesting = self.frame_saver.save_interesting_frame(curiosities)
interesting_episode = interesting_episode or interesting
obs = next_obs
episode_length += 1
done = all(dones)
if ep % self.arglist.eval_frequency == 0:
eval_rewards = np.zeros((self.arglist.eval_episodes, n_agents))
for i in range(self.arglist.eval_episodes):
ep_rewards, _, _ = self.eval(ep, n_agents)
eval_rewards[i, :] = ep_rewards
if self.arglist.alg == "maddpg":
self.logger.log_episode(
ep,
eval_rewards.mean(0),
eval_rewards.var(0),
self.alg.agents[0].get_exploration_scale(),
)
if self.arglist.alg == "iql":
self.logger.log_episode(
ep, eval_rewards.mean(0), eval_rewards.var(0), self.alg.agents[0].epsilon
)
self.logger.dump_episodes(1)
if ep % 100 == 0 and ep > 0:
duration = time.time() - start_time
self.logger.dump_train_progress(ep, self.arglist.num_episodes, duration)
if interesting_episode:
self.frame_saver.save_episode_gif()
if ep % (self.arglist.save_interval // 2) == 0 and ep > 0:
# update plots
self.plotter.update_reward_plot(self.arglist.plot)
self.plotter.update_exploration_plot(self.arglist.plot)
self.plotter.update_alg_loss_plot(self.arglist.plot)
if self.arglist.curiosity is not None:
self.plotter.update_cur_loss_plot(self.arglist.plot)
self.plotter.update_intrinsic_reward_plot(self.arglist.plot)
if ep % self.arglist.save_interval == 0 and ep > 0:
# save plots
print("Remove previous plots")
self.plotter.clear_plots()
print("Saving intermediate plots")
self.plotter.save_reward_plot(str(ep))
self.plotter.save_exploration_plot(str(ep))
self.plotter.save_alg_loss_plots(str(ep))
self.plotter.save_cur_loss_plots(str(ep))
self.plotter.save_intrinsic_reward_plot(str(ep))
# save models
print("Remove previous models")
self.model_saver.clear_models()
print("Saving intermediate models")
self.model_saver.save_models(self.alg, str(ep))
# save logs
print("Remove previous logs")
self.logger.clear_logs()
print("Saving intermediate logs")
self.logger.save_episodes(extension=str(ep))
self.logger.save_losses(extension=str(ep))
# save parameter log
self.logger.save_parameters(
env_name,
task_name,
n_agents,
observation_sizes,
action_sizes,
discrete_actions,
self.arglist,
)
duration = time.time() - start_time
print("Overall duration: %.2fs" % duration)
print("Remove previous plots")
self.plotter.clear_plots()
print("Saving final plots")
self.plotter.save_reward_plot("final")
self.plotter.save_exploration_plot("final")
self.plotter.save_alg_loss_plots("final")
self.plotter.save_cur_loss_plots("final")
self.plotter.save_intrinsic_reward_plot("final")
# save models
print("Remove previous models")
self.model_saver.clear_models()
print("Saving final models")
self.model_saver.save_models(self.alg, "final")
# save logs
print("Remove previous logs")
self.logger.clear_logs()
print("Saving final logs")
self.logger.save_episodes(extension="final")
self.logger.save_losses(extension="final")
self.logger.save_duration_cuda(duration, torch.cuda.is_available())
# save parameter log
self.logger.save_parameters(
env_name,
task_name,
n_agents,
observation_sizes,
action_sizes,
discrete_actions,
self.arglist,
)
env.close()
if __name__ == "__main__":
train = Train()
train.train()