avg_reward_list = []
for e, epsilon in enumerate(epsilon_list):
print "Epsilon: {0}".format(epsilon)
qlearner = QLearner(mdp1, initial_state1, epsilon=epsilon, alpha=learning_rate)
epoch_list.append(range(num_learning_epochs))
avg_reward_list.append([])
for epoch in epoch_list[e]:
for trial in range(num_learning_trials):
qlearner.run_learning_trial()
avg_reward = 0
for trial in range(num_simulation_trials):
(total_reward, state_seq, action_seq) = qlearner.run_simulation_trial()
avg_reward += total_reward
avg_reward = 1.*avg_reward/num_simulation_trials
avg_reward_list[e].append(avg_reward)
print "MDP1 epoch {0}: {1}".format(epoch, avg_reward)
Plot.plot_multiple(epoch_list, avg_reward_list, [str(e) for e in epsilon_list], 'epsilon', 'MDP1 Learning: Epsilon', 'mdp1_epsilon_plot.png')
print
### PART III: MDP 1 alpha experiments
epsilon = 0.25
learning_rate_list = [0.001, 0.01, 0.1, 1.0]
epoch_list = []
avg_reward_list = []
qlearner = QLearner(mdp1,
initial_state1,
epsilon=epsilon,
alpha=learning_rate)
epoch_list.append(range(num_learning_epochs))
avg_reward_list.append([])
for epoch in epoch_list[e]:
for trial in range(num_learning_trials):
qlearner.run_learning_trial()
avg_reward = 0
for trial in range(num_simulation_trials):
(total_reward, state_seq,
action_seq) = qlearner.run_simulation_trial()
avg_reward += total_reward
avg_reward = 1. * avg_reward / num_simulation_trials
avg_reward_list[e].append(avg_reward)
print "MDP1 epoch {0}: {1}".format(epoch, avg_reward)
Plot.plot_multiple(epoch_list, avg_reward_list, [str(e) for e in epsilon_list],
'epsilon', 'MDP1 Learning: Epsilon',
'mdp1_epsilon_plot.png')
print
### PART III: MDP 1 alpha experiments
epsilon = 0.25
learning_rate_list = [0.001, 0.01, 0.1, 1.0]
epoch_list = []
avg_reward_list = []
