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ruff and isort #9

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396 changes: 275 additions & 121 deletions scripts/explore_parameters.py
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210 changes: 123 additions & 87 deletions scripts/test_simple_gridworld.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,106 +12,139 @@
"""

import sys
sys.path.insert(0,'./src/')

sys.path.insert(0, "./src/")

import time

from numpy import random

from world_model import SimpleGridworld
from environments.very_simple_gridworlds import make_simple_gridworld
import pylab as plt
from satisfia.agents.makeMDPAgentSatisfia import AgentMDPPlanning
from world_model import SimpleGridworld


def move_randomly(env):
state, info = env.reset()
delta = terminated = 0
for t in range(1000):
actions = env.possible_actions(state)
action = random.choice(actions)
print(t, state, delta, terminated, info, actions, action)
state, delta, terminated, _, info = env.step(action)
if terminated:
print(t, state, delta, terminated)
print("Goal reached!")
break
state, info = env.reset()
delta = terminated = 0
for t in range(1000):
actions = env.possible_actions(state)
action = random.choice(actions)
print(t, state, delta, terminated, info, actions, action)
state, delta, terminated, _, info = env.step(action)
if terminated:
print(t, state, delta, terminated)
print("Goal reached!")
break


def move_agent(env, aleph):
class policy():
def __init__(self):
pass

def __call__(self, state):
return self

def score(self, action):
return 1

state, info = env.reset()
agent = AgentMDPPlanning({
# admissibility parameters:
"maxLambda": 1, # upper bound on local relative aspiration in each step (must be minLambda...1) # TODO: rename to lambdaHi
"minLambda": 0, # lower bound on local relative aspiration in each step (must be 0...maxLambda) # TODO: rename to lambdaLo
# policy parameters:
"lossTemperature": 1e-10, # temperature of softmin mixture of actions w.r.t. loss, must be > 0
"lossCoeff4Random": 0, # weight of random tie breaker in loss function, must be >= 0
"lossCoeff4FeasibilityPower": 0, # weight of power of squared admissibility interval width in loss function, must be >= 0
"lossCoeff4LRA1": 0, # weight of current-state deviation of LRA from 0.5 in loss function, must be >= 0
"lossCoeff4Time1": 0, # weight of not terminating in loss function, must be >= 0
"lossCoeff4Entropy1": 0, # weight of current-state action entropy in loss function, must be >= 0
"lossCoeff4KLdiv1": 0, # weight of current-state KL divergence in loss function, must be >= 0
"lossCoeff4DP": 0, # weight of disordering potential in loss function, must be >= 0
"uninformedStatePriorScore": 0,
"internalTransitionEntropy": 0,
# coefficients for expensive to compute loss functions (all zero by default except for variance):
"lossCoeff4Variance": 0, # weight of variance of total in loss function, must be >= 0
"lossCoeff4Fourth": 0, # weight of centralized fourth moment of total in loss function, must be >= 0
"lossCoeff4Cup": 0, # weight of "cup" loss component, based on sixth moment of total, must be >= 0
"lossCoeff4LRA": 0, # weight of deviation of LRA from 0.5 in loss function, must be >= 0
"lossCoeff4Time": 100, # weight of time in loss function, must be >= 0
"lossCoeff4DeltaVariation": 0, # weight of variation of Delta in loss function, must be >= 0
"lossCoeff4Entropy": 0, # weight of action entropy in loss function, must be >= 0
"lossCoeff4KLdiv": 0, # weight of KL divergence in loss function, must be >= 0
"lossCoeff4OtherLoss": 0, # weight of other loss components specified by otherLossIncrement, must be >= 0
"uninformedPolicy": policy()
}, world=env)
total = delta
for t in range(1000):
action, aleph4action = agent.localPolicy(state, aleph).sample()[0]
print("t:",t, ", last delta:",delta, ", total:", total, ", s:",state, ", aleph4s:", aleph, ", a:", action, ", aleph4a:", aleph4action)
nextState, delta, terminated, _, info = env.step(action)
total += delta
aleph = agent.propagateAspiration(state, action, aleph4action, delta, nextState)
state = nextState
if terminated:
print("t:",t, ", last delta:",delta, ", final total:", total, ", final s:",state, ", aleph4s:", aleph)
print("Terminated.")
break
class policy:
def __init__(self):
pass

def __call__(self, state):
return self

def score(self, action):
return 1

state, info = env.reset()
agent = AgentMDPPlanning(
{
# admissibility parameters:
"maxLambda": 1, # upper bound on local relative aspiration in each step (must be minLambda...1) # TODO: rename to lambdaHi
"minLambda": 0, # lower bound on local relative aspiration in each step (must be 0...maxLambda) # TODO: rename to lambdaLo
# policy parameters:
"lossTemperature": 1e-10, # temperature of softmin mixture of actions w.r.t. loss, must be > 0
"lossCoeff4Random": 0, # weight of random tie breaker in loss function, must be >= 0
"lossCoeff4FeasibilityPower": 0, # weight of power of squared admissibility interval width in loss function, must be >= 0
"lossCoeff4LRA1": 0, # weight of current-state deviation of LRA from 0.5 in loss function, must be >= 0
"lossCoeff4Time1": 0, # weight of not terminating in loss function, must be >= 0
"lossCoeff4Entropy1": 0, # weight of current-state action entropy in loss function, must be >= 0
"lossCoeff4KLdiv1": 0, # weight of current-state KL divergence in loss function, must be >= 0
"lossCoeff4DP": 0, # weight of disordering potential in loss function, must be >= 0
"uninformedStatePriorScore": 0,
"internalTransitionEntropy": 0,
# coefficients for expensive to compute loss functions (all zero by default except for variance):
"lossCoeff4Variance": 0, # weight of variance of total in loss function, must be >= 0
"lossCoeff4Fourth": 0, # weight of centralized fourth moment of total in loss function, must be >= 0
"lossCoeff4Cup": 0, # weight of "cup" loss component, based on sixth moment of total, must be >= 0
"lossCoeff4LRA": 0, # weight of deviation of LRA from 0.5 in loss function, must be >= 0
"lossCoeff4Time": 100, # weight of time in loss function, must be >= 0
"lossCoeff4DeltaVariation": 0, # weight of variation of Delta in loss function, must be >= 0
"lossCoeff4Entropy": 0, # weight of action entropy in loss function, must be >= 0
"lossCoeff4KLdiv": 0, # weight of KL divergence in loss function, must be >= 0
"lossCoeff4OtherLoss": 0, # weight of other loss components specified by otherLossIncrement, must be >= 0
"uninformedPolicy": policy(),
},
world=env,
)
total = delta
for t in range(1000):
action, aleph4action = agent.localPolicy(state, aleph).sample()[0]
print(
"t:",
t,
", last delta:",
delta,
", total:",
total,
", s:",
state,
", aleph4s:",
aleph,
", a:",
action,
", aleph4a:",
aleph4action,
)
nextState, delta, terminated, _, info = env.step(action)
total += delta
aleph = agent.propagateAspiration(state, action, aleph4action, delta, nextState)
state = nextState
if terminated:
print(
"t:",
t,
", last delta:",
delta,
", final total:",
total,
", final s:",
state,
", aleph4s:",
aleph,
)
print("Terminated.")
break


for gw in ["GW1", "GW2", "GW3", "GW4", "GW5", "GW6"]:
print("\nRUNNING AROUND",gw,":")
env, aleph0 = make_simple_gridworld(gw = gw, render_mode = "human", fps = 1)
env.reset()
#move_randomly(env)
move_agent(env, aleph0)
env.render()
#time.sleep(5)
env.close()
print("\nRUNNING AROUND", gw, ":")
env, aleph0 = make_simple_gridworld(gw=gw, render_mode="human", fps=1)
env.reset()
# move_randomly(env)
move_agent(env, aleph0)
env.render()
# time.sleep(5)
env.close()


print("\nPUSHING A BOX THROUGH A GOAL:")
env, aleph0 = make_simple_gridworld(gw = "test_box", render_mode = "human", fps = 1)
env, aleph0 = make_simple_gridworld(gw="test_box", render_mode="human", fps=1)
env.reset()
#move_randomly(env)
# move_randomly(env)
move_agent(env, aleph0)
env.render()
time.sleep(1)
env.close()


print("\nRUNNING AROUND AISG2:")
env, aleph0 = make_simple_gridworld(gw = "AISG2", render_mode = "human", fps = 1)
env, aleph0 = make_simple_gridworld(gw="AISG2", render_mode="human", fps=1)
env.reset()
#move_randomly(env)
# move_randomly(env)
move_agent(env, aleph0)
env.render()
time.sleep(1)
Expand All @@ -121,23 +154,26 @@ def score(self, action):

print("\nRUNNING AROUND A RANDOM GRID:")
grid = [
[
random.choice([' ', ' ', ' ', '#', '#', ',', '^', '~','X'])
for x in range(11)
]
for y in range(11)
[random.choice([" ", " ", " ", "#", "#", ",", "^", "~", "X"]) for x in range(11)]
for y in range(11)
]
grid[2][2] = "A"
grid[8][8] = "G"
delta_grid = [
[
' ' if grid[y][x] == '#' else random.choice([' ','M','P'], p=[0.4,0.3,0.3])
for x in range(11)
]
for y in range(11)
[
" " if grid[y][x] == "#" else random.choice([" ", "M", "P"], p=[0.4, 0.3, 0.3])
for x in range(11)
]
for y in range(11)
]
print(grid)
print(delta_grid)
env = SimpleGridworld(grid = grid, delta_grid = delta_grid, cell_code2delta = {'M':-1, 'P':1}, render_mode = "human", fps = 1)
env = SimpleGridworld(
grid=grid,
delta_grid=delta_grid,
cell_code2delta={"M": -1, "P": 1},
render_mode="human",
fps=1,
)
move_randomly(env)
env.close()
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