# Environment
sizeX = 4
sizeY = 4
defaultReward = -1.0
terminalStates = [(0, 0), (3, 3)]
# Agent
gamma = 1.0
thresh_convergence = 1e-30
n = 5
alpha_TDn = 0.01
alpha_TD = 0.01
alpha_sumTDError = 0.01
env = DeterministicGridWorld(sizeX,
sizeY,
defaultReward=defaultReward,
terminalStates=terminalStates)
policy = StochasticPolicy(env.nStates, env.nActions)
agent_PE = PolicyEvaluation(env.nStates, env.nActions, gamma,
thresh_convergence, env.computeExpectedValue)
# TD agent to validate the TDn implementation
agent_TD = TDPrediction(env.nStates, alpha_TD, gamma)
agent_TDn = nStepTDPrediction(env.nStates, alpha_TDn, gamma, n)
env.printEnv()
# Policy evaluation for reference
for e in range(nEpisodes):
deltaMax, isConverged = agent_PE.evaluate(policy)
epsilon_SARSA = 0.1
epsilon_QLearning = 0.1
epsilon_ExpectedSARSA = 0.1
epsilon_DoubleQLearning = 0.1
avg_reward_sums_SARSA = np.zeros(nEpisodes)
avg_reward_sums_QLearning = np.zeros(nEpisodes)
avg_reward_sums_ExpectedSARSA = np.zeros(nEpisodes)
avg_reward_sums_DoubleQLearning = np.zeros(nEpisodes)
for idx_experiment in range(1, nExperiments + 1):
print("Experiment : ", idx_experiment)
env = DeterministicGridWorld(sizeX,
sizeY,
specialRewards=specialRewards,
defaultReward=defaultReward,
terminalStates=terminalStates,
startStates=startStates)
agent_SARSA = SARSA(env.nStates,
env.nActions,
alpha_SARSA,
gamma_SARSA,
epsilon=epsilon_SARSA)
agent_QLearning = QLearning(env.nStates,
env.nActions,
alpha_QLearning,
gamma_QLearning,
epsilon=epsilon_QLearning)
agent_ExpectedSARSA = ExpectedSARSA(env.nStates,
env.nActions,
gamma_DynaQ_ASB = 0.95
epsilon_DynaQ_ASB = 0.2
nPlanningSteps_DynaQ_ASB = 5
kappa_ASB = 0.001
avg_cum_reward_DynaQ = np.zeros(nTimesteps)
avg_cum_reward_DynaQPlus = np.zeros(nTimesteps)
avg_cum_reward_DynaQ_ASB = np.zeros(nTimesteps)
for idxExperiment in range(1, nExperiments + 1):
print("Experiment:", idxExperiment)
env = DeterministicGridWorld(sizeX,
sizeY,
startStates=startStates,
terminalStates=terminalStates,
impassableStates=impassableStates,
defaultReward=defaultReward,
specialRewards=specialRewards)
agent_DynaQ = DynaQ(env.nStates,
env.nActions,
alpha_DynaQ,
gamma_DynaQ,
nPlanningSteps_DynaQ,
kappa=kappa_DynaQ,
epsilon=epsilon_DynaQ)
cumulative_reward_DynaQ, nStepsPerEpisode_DynaQ = runExperiment(
nEpisodes, nTimesteps, env, agent_DynaQ, envChangeTimestep)
avg_cum_reward_DynaQ += (1.0 / idxExperiment) * (
cumulative_reward_DynaQ - avg_cum_reward_DynaQ)
nEpisodes = 1000
# Environment
sizeX = 5
sizeY = 5
defaultReward = 0.0
outOfGridReward = -1.0
specialRewards = {((1,0),0):10,((1,0),1):10,((1,0),2):10,((1,0),3):10,
((3,0),0):5, ((3,0),1):5, ((3,0),2):5, ((3,0),3):5}
specialStateTransitions = {(1,0):(1,4), (3,0):(3,2)}
# Agent
gamma = 0.9
thresh_convergence = 1e-30
env = DeterministicGridWorld(sizeX, sizeY, specialRewards=specialRewards,
specialStateTransitions=specialStateTransitions, defaultReward=defaultReward, outOfGridReward=outOfGridReward)
agent = PolicyIteration(env.nStates, env.nActions, gamma, thresh_convergence, env.computeExpectedValue)
env.printEnv()
for e in range(nEpisodes):
deltaMax, isConverged, isPolicyStable = agent.update()
print("Episode : ", e, " Delta: ", deltaMax, " isConverged: ", isConverged, " isPolicyStable: ", isPolicyStable)
print()
printStr = ""
for y in range(sizeY):
for x in range(sizeX):
i = env.getLinearIndex(x,y)
printStr = printStr + "{:.2f}".format(agent.valueTable[i]) + "\t"
if __name__ == "__main__":
nEpisodes = 1000
# Environment
sizeX = 4
sizeY = 4
defaultReward = -1.0
terminalStates = [(0, 0), (3, 3)]
# Agent
gamma = 1.0
thresh_convergence = 1e-30
env = DeterministicGridWorld(sizeX,
sizeY,
defaultReward=defaultReward,
terminalStates=terminalStates)
policy = StochasticPolicy(env.nStates, env.nActions)
agent = PolicyEvaluation(env.nStates, env.nActions, gamma,
thresh_convergence, env.computeExpectedValue)
env.printEnv()
for e in range(nEpisodes):
deltaMax, isConverged = agent.evaluate(policy)
print("Episode : ", e, " Delta: ", deltaMax)
printStr = ""
for y in range(sizeY):
for x in range(sizeX):
if __name__=="__main__": nEpisodes = 1000 # Environment sizeX = 4 sizeY = 5 defaultReward = -1.0 terminalStates= [(0,0), (3,3)] doUnblockAdditionalState = True # Set true for the second part of exercise 4.2 # Agent gamma = 0.9 thresh_convergence = 1e-30 env = DeterministicGridWorld(sizeX, sizeY, defaultReward=defaultReward, terminalStates=terminalStates) # First part of exercise 4.2: addition of new state env.stateTransitionProbs[12,:,16:]=0.0 env.stateTransitionProbs[13,:,16:]=0.0 env.stateTransitionProbs[14,:,16:]=0.0 env.stateTransitionProbs[15,:,16:]=0.0 env.stateTransitionProbs[16,:,:]=0.0 env.stateTransitionProbs[18,:,:]=0.0 env.stateTransitionProbs[19,:,:]=0.0 # State 15 of the question is indexed as 17 env.stateTransitionProbs[17,:,:]=0.0 env.stateTransitionProbs[17,0,13]=1.0 #N env.stateTransitionProbs[17,1,17]=1.0 #S
print("Scale : ", scale)
sizeX_scaled, sizeY_scaled, startStates_scaled, terminalStates_scaled, impassableStates_scaled = scaleEnv(
scale, sizeX, sizeY, startStates, terminalStates,
impassableStates_bounds)
specialRewards_scaled = {
((terminalStates_scaled[0][0], terminalStates_scaled[0][1] + 1), 0):
1.0,
((terminalStates_scaled[0][0] - 1, terminalStates_scaled[0][1]), 2):
1.0
}
env = DeterministicGridWorld(sizeX_scaled,
sizeY_scaled,
startStates=startStates_scaled,
terminalStates=terminalStates_scaled,
impassableStates=impassableStates_scaled,
defaultReward=defaultReward,
specialRewards=specialRewards_scaled)
env.printEnv()
agent_PS = PrioritizedSweeping(env.nStates,
env.nActions,
alpha_PS,
gamma_PS,
nPlanningSteps_PS,
theta=theta_PS,
epsilon=epsilon_PS)
cumulative_reward_PS, nStepsPerEpisode_PS, nUpdates_PS = runExperiment(
nTimesteps,
