alpha_min=0.001,
epsilon=1.0,
epsilon_min=0.1,
epsilon_decay=0.99,
n_iter=10000,
skip_check=False,
iter_callback=None,
run_stat_frequency=None)
ql.run()
# print('q learning Q matrix:', ql.Q)
print('q learning value function:', ql.V)
# print('q learning mean discrepancy:', ql.mean_discrepancy)
print('q learning best policy:', ql.policy)
results.append(ql)
plot_rewards(disc, results, 'Q-Learning Discount/Rewards FrozenLake',
'q_learning_discount_rewards_frozenlake', 'Discount')
results = []
for e in ep:
ql = QLearning(
P, # transitions
R, # rewards
0.55, # discount
alpha=0.1,
alpha_decay=0.99,
alpha_min=0.001,
epsilon=e,
epsilon_min=0.000001,
epsilon_decay=0.99,
n_iter=10000,
skip_check=False,
pi = PolicyIteration(
# pi = PolicyIterationModified(
P, # transitions
R, # rewards
d, # discount
# epsilon=0.01,
max_iter=1000,
)
pi.run()
print('policy iteration value function:', pi.V)
print('policy iteration iterations:', pi.iter)
print('policy iteration time:', pi.time)
print('policy iteration best policy:', pi.policy)
results.append(pi)
plot_rewards(disc, results, 'Policy Iteration Discount/Rewards FrozenLake',
'policy_iteration_discount_rewards_frozenlake', 'Discount')
results = []
for e in ep:
pi = PolicyIteration(
# pi = PolicyIterationModified(
P, # transitions
R, # rewards
0.9, # discount
# epsilon=e,
max_iter=1000,
)
pi.epsilon = e
pi.run()
print('policy iteration value function:', pi.V)
print('policy iteration iterations:', pi.iter)
print('policy iteration time:', pi.time)
ep = [0.00099, 0.001, 0.005, 0.01, 0.03]
ex = OpenAI_MDPToolbox('FrozenLake-v0', False)
P = ex.P
R = ex.R
results = []
for d in disc:
vi = ValueIteration(P, R, d, epsilon=0.001, max_iter=1000)
vi.run()
print('value iteration value function:', vi.V)
print('value iteration iterations:', vi.iter)
print('value iteration time:', vi.time)
print('value iteration best policy:', vi.policy)
results.append(vi)
plot_rewards(disc, results, 'Value Iteration Discount/Rewards FrozenLake',
'value_iteration_discount_rewards_frozenlake', 'Discount')
results = []
for e in ep:
vi = ValueIteration(P, R, 0.9, epsilon=e, max_iter=1000)
vi.run()
print('value iteration value function:', vi.V)
print('value iteration iterations:', vi.iter)
print('value iteration time:', vi.time)
print('value iteration best policy:', vi.policy)
results.append(vi)
plot_rewards(ep, results, 'Value Iteration Epsilon/Rewards FrozenLake',
'value_iteration_epsilon_rewards_frozenlake', 'Epsilon')
print('----------------Best VI FrozenLake---------------')
pi = PolicyIteration(
# pi = PolicyIterationModified(
P, # transitions
R, # rewards
d, # discount
max_iter=1000,
)
pi.run()
print('policy iteration value function:', pi.V)
print('policy iteration iterations:', pi.iter)
print('policy iteration time:', pi.time)
print('policy iteration best policy:', pi.policy)
results.append(pi)
plot_rewards(
disc, results, 'Policy Iteration Discount/Rewards Forest',
'policy_iteration_discount_rewards_forest', 'Discount'
)
results = []
for e in ep:
pi = PolicyIteration(
# pi = PolicyIterationModified(
P, # transitions
R, # rewards
0.9, # discount
# epsilon=e,
max_iter=1000,
)
pi.epsilon = e
pi.run()
print('policy iteration value function:', pi.V)
print('policy iteration iterations:', pi.iter)
disc = [0.1, 0.3, 0.5, 0.7, 0.9]
ep = [0.00099, 0.001, 0.005, 0.01, 0.03]
P, R = mdptoolbox.example.forest(S=500, r1=100, r2=2, p=0.1, is_sparse=False)
results = []
for d in disc:
vi = ValueIteration(P, R, d, epsilon=0.001, max_iter=1000)
vi.run()
print('value iteration value function:', vi.V)
print('value iteration iterations:', vi.iter)
print('value iteration time:', vi.time)
print('value iteration best policy:', vi.policy)
results.append(vi)
plot_rewards(disc, results, 'Value Iteration Discount/Rewards Forest',
'value_iteration_discount_rewards_forest', 'Discount')
results = []
for e in ep:
vi = ValueIteration(P, R, 0.9, epsilon=e, max_iter=1000)
vi.run()
print('value iteration value function:', vi.V)
print('value iteration iterations:', vi.iter)
print('value iteration time:', vi.time)
print('value iteration best policy:', vi.policy)
results.append(vi)
plot_rewards(ep, results, 'Value Iteration Epsilon/Rewards Forest',
'value_iteration_epsilon_rewards_forest', 'Epsilon')
print('----------------Best VI Forest---------------')