def policy_improve_on_click(obj):
plt.close()
fig, ax = plt.subplots(ncols=2, figsize=(14, 6))
output.clear_output(True)
policy_improvement(env, policy, value_table)
with output:
plot_value_table(env, value_table, ax=ax[0], vmin=vmin, vmax=vmax)
plot_policy(env, policy, ax=ax[1])
plt.show()
def finish_iteration(self):
initial_i = self.i
while initial_i == self.i:
self.next_step(fast_execution=True)
# Plot the resulting state of the env, value table, and policy
self.fig, self.ax = plt.subplots(ncols=3, figsize=(20, 6))
plot_s_pmf(self.env, self.s, self.a, ax=self.ax[0])
plot_value_table(self.env,
self.value_table,
ax=self.ax[1],
vmin=-8,
vmax=0)
plot_policy(self.env, self.policy, ax=self.ax[2])
plt.show()
def finish_episode(self):
print_string = ''
current_i = self.i
while self.i == current_i:
print_string += f'{self.next_step(fast_execution=True)}'
self.fig, self.ax = plt.subplots(ncols=3, figsize=(20, 6))
plot_all_states(self.env, self.ax[0])
plot_agent(self.s, self.ax[0])
plot_value_table(self.env,
self.value_table,
self.ax[1],
vmin=self.vmin,
vmax=self.vmax)
plot_policy(self.env, self.policy, self.ax[2])
plt.show()
print(print_string)
def policy_iteration_wrapper():
env.reset()
value_table = np.zeros(9)
policy = np.array([[1, 0, 0, 0], [0.5, 0.5, 0, 0], [0, 1, 0, 0],
[0, 0, 0, 1], [0, 0, 1, 0], [0, 1, 0, 0], [0, 0, 1, 0],
[0, 0, 1, 0], [1, 0, 0, 0]])
btn_eval = widgets.Button(description='Policy eval iteration')
btn_improve = widgets.Button(description='Policy improvement')
display(btn_eval)
display(btn_improve)
output = widgets.Output()
vmin = -8
vmax = 0
with output:
fig, ax = plt.subplots(ncols=2, figsize=(14, 6))
plot_value_table(env, value_table, ax=ax[0], vmin=vmin, vmax=vmax)
plot_policy(env, policy, ax=ax[1])
plt.show()
def policy_eval_on_click(obj):
plt.close()
fig, ax = plt.subplots(ncols=2, figsize=(14, 6))
output.clear_output(True)
policy_evaluation_one_step(env, policy, value_table)
with output:
plot_value_table(env, value_table, ax=ax[0], vmin=vmin, vmax=vmax)
plot_policy(env, policy, ax=ax[1])
plt.show()
def policy_improve_on_click(obj):
plt.close()
fig, ax = plt.subplots(ncols=2, figsize=(14, 6))
output.clear_output(True)
policy_improvement(env, policy, value_table)
with output:
plot_value_table(env, value_table, ax=ax[0], vmin=vmin, vmax=vmax)
plot_policy(env, policy, ax=ax[1])
plt.show()
btn_eval.on_click(policy_eval_on_click)
btn_improve.on_click(policy_improve_on_click)
display(output)
def next_step(self, fast_execution=False):
print_string = ''
if self.phase == 'choosing_actions':
if not fast_execution:
self.fig, self.ax = plt.subplots(ncols=3, figsize=(20, 6))
plot_env_agent_and_policy_at_state(self.env, self.s,
self.policy, self.ax[0])
plot_value_table(self.env,
self.value_table,
self.ax[1],
vmin=self.vmin,
vmax=self.vmax)
plot_policy(self.env, self.policy, self.ax[2])
plt.show()
print('Sampling action...\n')
self.phase = 'showing_sampled_action'
elif self.phase == 'showing_sampled_action':
self.sampled_a = np.random.choice(4, p=self.policy[self.s_idx])
if not fast_execution:
self.fig, self.ax = plt.subplots(ncols=3, figsize=(20, 6))
plot_env_agent_and_chosen_action(self.env, self.s,
self.sampled_a, self.ax[0])
plot_value_table(self.env,
self.value_table,
self.ax[1],
vmin=self.vmin,
vmax=self.vmax)
plot_policy(self.env, self.policy, self.ax[2])
plt.show()
print(f'Action sampled: {a_index_to_symbol[self.sampled_a]}\n')
self.phase = 'carrying_out_action'
elif self.phase == 'carrying_out_action':
old_s = self.s
s_prime, r, t = self.env.step(self.sampled_a)
self.current_episode.append([old_s, self.sampled_a, r, s_prime, t])
if not fast_execution:
self.fig, self.ax = plt.subplots(ncols=3, figsize=(20, 6))
plot_all_states(self.env, self.ax[0])
plot_agent(old_s, self.ax[0], alpha=0.3)
plot_agent(self.s, self.ax[0])
plot_value_table(self.env,
self.value_table,
self.ax[1],
vmin=self.vmin,
vmax=self.vmax)
plot_policy(self.env, self.policy, self.ax[2])
plt.show()
print(
f'Transition: <{old_s}, {a_index_to_symbol[self.sampled_a]}, {r:.1f}, {s_prime}>'
)
self.phase = 'showing_td_target'
elif self.phase == 'showing_td_target':
if not fast_execution:
self.fig, self.ax = plt.subplots(ncols=3, figsize=(20, 6))
plot_all_states(self.env, self.ax[0])
plot_agent(self.s, self.ax[0])
plot_value_table(self.env,
self.value_table,
self.ax[1],
vmin=self.vmin,
vmax=self.vmax)
plot_policy(self.env, self.policy, self.ax[2])
plt.show()
old_s, a, r, s_prime, _ = self.current_episode[-1]
s_prime_idx = self.env.states.index(s_prime)
self.td_target = r + 0.9 * self.value_table[s_prime_idx]
if not fast_execution:
print(
f'Transition: <{old_s}, {a_index_to_symbol[a]}, {r:.1f}, {s_prime}>\n'
)
print('Updating value table:')
print(
f'TD target = R + 𝛾 * V({s_prime}) = {r:.1f} + 0.9*{self.value_table[s_prime_idx]:.2f} = '
f'{self.td_target:.2f}')
self.phase = 'updating_value_table'
elif self.phase == 'updating_value_table':
if not fast_execution:
self.fig, self.ax = plt.subplots(ncols=3, figsize=(20, 6))
plot_all_states(self.env, self.ax[0])
plot_agent(self.s, self.ax[0])
plot_value_table(self.env,
self.value_table,
self.ax[1],
vmin=self.vmin,
vmax=self.vmax)
plot_policy(self.env, self.policy, self.ax[2])
plt.show()
old_s, a, r, s_prime, _ = self.current_episode[-1]
old_s_idx = self.env.states.index(old_s)
new_value = self.alpha * self.td_target + (
1 - self.alpha) * self.value_table[old_s_idx]
if fast_execution:
print_string += f'Transition: <{old_s}, {a_index_to_symbol[a]}, {r:.1f}, {s_prime}>\n'
print_string += f'TD target = {self.td_target:.2f}\n'
print_string += f'V({old_s}) ← {self.value_table[old_s_idx]:.2f} + {self.alpha:.2f} * ' \
f'({self.td_target:.2f} - {self.value_table[old_s_idx]:.2f}) = {new_value:.2f}\n\n'
else:
print(
f'Transition: <{old_s}, {a_index_to_symbol[a]}, {r:.1f}, {s_prime}>\n'
)
print('Updating value table:')
print(f'TD target = {self.td_target:.2f}')
print(
f'V({old_s}) ← {self.value_table[old_s_idx]:.2f} + {self.alpha:.2f} * '
f'({self.td_target:.2f} - {self.value_table[old_s_idx]:.2f}) = {new_value:.2f}\n'
)
self.value_table[old_s_idx] = new_value
self.phase = 'showing_new_value_table'
elif self.phase == 'showing_new_value_table':
if not fast_execution:
self.fig, self.ax = plt.subplots(ncols=3, figsize=(20, 6))
plot_all_states(self.env, self.ax[0])
plot_agent(self.s, self.ax[0])
plot_value_table(self.env,
self.value_table,
self.ax[1],
vmin=self.vmin,
vmax=self.vmax)
plot_policy(self.env, self.policy, self.ax[2])
plt.show()
old_s, a, r, s_prime, t = self.current_episode[-1]
old_s_idx = self.env.states.index(old_s)
if not fast_execution:
print(
f'Transition: <{old_s}, {a_index_to_symbol[a]}, {r:.1f}, {s_prime}>\n'
)
print('Updating value table:')
print(f'TD target = {self.td_target:.2f}')
print(f'V({old_s}) = {self.value_table[old_s_idx]:.2f}')
if t:
if not fast_execution:
print('\nTerminal state, reseting environment.')
self.env.reset()
self.current_episode = []
self.i += 1
self.phase = 'choosing_actions'
else:
raise ValueError(f'Phase {self.phase} not recognized.')
return print_string
# Set policy to greedy w.r.t. Q-values computed above
new_policy[np.argmax(q)] = 1
policy[i_s] = new_policy
def epsilon_greedy_pi_from_q_table(env, q_table, epsilon):
policy = np.zeros((len(env.states), 4))
for i_s, s in enumerate(env.states):
q_values = q_table[i_s]
a_star = np.argmax(q_values)
policy[i_s, :] = [epsilon / 4] * 4
policy[i_s, a_star] += 1 - epsilon
return policy
if __name__ == '__main__':
from lib.grid_world import grid_world_3x3 as env
from lib.plot_utils import plot_policy, plot_value_table
import matplotlib.pyplot as plt
np.random.seed(2)
env.reset()
value_table = np.random.rand(130)
policy = np.ones((len(env.states), 4)) * 0.25
for i, s in enumerate(env.states):
policy[i, :] = np.random.dirichlet([0.1, 0.1, 0.1, 0.1])
plot_value_table(env, value_table)
plot_policy(env, policy)
plt.show()
def next_step(self, fast_execution=False):
print_string = ''
if self.phase == 'choosing_actions':
if not fast_execution:
self.fig, self.ax = plt.subplots(ncols=3, figsize=(20, 6))
plot_env_agent_and_policy_at_state(self.env, self.s,
self.policy, self.ax[0])
plot_value_table(self.env,
self.value_table,
self.ax[1],
vmin=self.vmin,
vmax=self.vmax)
plot_policy(self.env, self.policy, self.ax[2])
plt.show()
print('Sampling action...\n')
print(self.get_current_episode_as_string())
self.phase = 'showing_sampled_action'
elif self.phase == 'showing_sampled_action':
self.sampled_a = np.random.choice(4, p=self.policy[self.s_idx])
if not fast_execution:
self.fig, self.ax = plt.subplots(ncols=3, figsize=(20, 6))
plot_env_agent_and_chosen_action(self.env, self.s,
self.sampled_a, self.ax[0])
plot_value_table(self.env,
self.value_table,
self.ax[1],
vmin=self.vmin,
vmax=self.vmax)
plot_policy(self.env, self.policy, self.ax[2])
plt.show()
print(f'Action sampled: {a_index_to_symbol[self.sampled_a]}\n')
print(self.get_current_episode_as_string())
self.phase = 'carrying_out_action'
elif self.phase == 'carrying_out_action':
old_s = self.s
s_prime, r, t = self.env.step(self.sampled_a)
self.current_episode.append([old_s, self.sampled_a, r, s_prime])
if not fast_execution:
self.fig, self.ax = plt.subplots(ncols=3, figsize=(20, 6))
plot_all_states(self.env, self.ax[0])
plot_agent(old_s, self.ax[0], alpha=0.3)
plot_agent(self.s, self.ax[0])
plot_value_table(self.env,
self.value_table,
self.ax[1],
vmin=self.vmin,
vmax=self.vmax)
plot_policy(self.env, self.policy, self.ax[2])
plt.show()
print(self.get_current_episode_as_string())
if t:
if not fast_execution:
print('A terminal state has been reached: end of episode.')
self.phase = 'computing_returns'
else:
self.phase = 'choosing_actions'
elif self.phase == 'computing_returns':
if not fast_execution:
self.fig, self.ax = plt.subplots(ncols=3, figsize=(20, 6))
plot_all_states(self.env, self.ax[0])
plot_agent(self.s, self.ax[0])
plot_value_table(self.env,
self.value_table,
self.ax[1],
vmin=self.vmin,
vmax=self.vmax)
plot_policy(self.env, self.policy, self.ax[2])
plt.show()
print(self.get_current_episode_as_string())
print('\nComputing returns:')
self.current_episode_returns = [0] * len(self.current_episode)
for i in reversed(range(len(self.current_episode))):
transition = self.current_episode[i]
if i == len(self.current_episode) - 1:
if not fast_execution:
print(
f'G_{i+1} = {transition[2]:.1f},\t\t\t\tat {transition[0]}'
)
self.current_episode_returns[i] = transition[2]
else:
self.current_episode_returns[i] = transition[
2] + 0.9 * self.current_episode_returns[i + 1]
if not fast_execution:
print(
f'G_{i+1} = {transition[2]:.1f} + 0.9 * {self.current_episode_returns[i+1]:.2f}',
end='')
print(
f' = {self.current_episode_returns[i]:.2f},\tat {transition[0]}'
)
self.phase = 'presenting_computed_returns'
elif self.phase == 'presenting_computed_returns':
if not fast_execution:
self.fig, self.ax = plt.subplots(ncols=3, figsize=(20, 6))
plot_all_states(self.env, self.ax[0])
plot_agent(self.s, self.ax[0])
plot_value_table(self.env,
self.value_table,
self.ax[1],
vmin=self.vmin,
vmax=self.vmax)
plot_policy(self.env, self.policy, self.ax[2])
plt.show()
print(self.get_current_episode_as_string())
print('\nComputing returns:')
for i, (transition, g) in reversed(
list(
enumerate(
zip(self.current_episode,
self.current_episode_returns)))):
print(f'G_{i+1} = {g:.2f},\tat {transition[0]}')
self.phase = 'updating_values'
elif self.phase == 'updating_values':
if fast_execution:
print_string += f'{self.get_current_episode_as_string()}\n'
else:
self.fig, self.ax = plt.subplots(ncols=3, figsize=(20, 6))
plot_all_states(self.env, self.ax[0])
plot_agent(self.s, self.ax[0])
plot_value_table(self.env,
self.value_table,
self.ax[1],
vmin=self.vmin,
vmax=self.vmax)
plot_policy(self.env, self.policy, self.ax[2])
plt.show()
print(self.get_current_episode_as_string())
print('\nComputing returns:')
# Print computed returns for all visited states
for i, (transition, g) in reversed(
list(
enumerate(
zip(self.current_episode,
self.current_episode_returns)))):
msg = f'G_{i+1} = {g:.2f},\tat {transition[0]}'
if fast_execution:
print_string += f'{msg}\n'
else:
print(msg)
if fast_execution:
print_string += '\n'
else:
print('\nUpdating values:')
for transition, g in zip(reversed(self.current_episode),
reversed(self.current_episode_returns)):
s_idx = self.env.states.index(transition[0])
new_value = self.alpha * g + (
1 - self.alpha) * self.value_table[s_idx]
cur_value = self.value_table[s_idx]
msg = f'V({transition[0]}) = {cur_value:.2f} + {self.alpha:.2f} * ({g:.2f} - {cur_value:.2f}) ' \
f'= {new_value:.2f}'
self.value_table[s_idx] = new_value
if fast_execution:
print_string += f'{msg}\n'
else:
print(msg)
self.phase = 'show_updated_value_function'
elif self.phase == 'show_updated_value_function':
if not fast_execution:
self.fig, self.ax = plt.subplots(ncols=3, figsize=(20, 6))
plot_all_states(self.env, self.ax[0])
plot_agent(self.s, self.ax[0])
plot_value_table(self.env,
self.value_table,
self.ax[1],
vmin=self.vmin,
vmax=self.vmax)
plot_policy(self.env, self.policy, self.ax[2])
plt.show()
print(self.get_current_episode_as_string())
print('\nComputing returns:')
for i, (transition, g) in reversed(
list(
enumerate(
zip(self.current_episode,
self.current_episode_returns)))):
print(f'G_{i+1} = {g:.2f},\tat {transition[0]}')
print('\nUpdating values:')
for transition, g in zip(
reversed(self.current_episode),
reversed(self.current_episode_returns)):
s_idx = self.env.states.index(transition[0])
print(
f'V({transition[0]}) = {self.value_table[s_idx]:.2f}')
self.current_episode = []
self.env.reset()
self.phase = 'choosing_actions'
else:
raise ValueError(f'Phase {self.phase} not recognized.')
return print_string
def next_step(self, fast_execution=False):
if not fast_execution:
# Shared plotting for all phases
self.fig, self.ax = plt.subplots(ncols=3, figsize=(20, 6))
plot_s_pmf(self.env, self.s, self.a, ax=self.ax[0])
plot_value_table(self.env,
self.value_table,
ax=self.ax[1],
vmin=-8,
vmax=0)
plot_policy(self.env, self.policy, ax=self.ax[2])
# Phase-specific plotting
if self.phase == 'computing_final_v' and self.env.terminal_table[
self.s_i] != 1:
self.ax[2].add_patch(
matplotlib.patches.Rectangle(self.s,
1.0,
1.0,
edgecolor='r',
facecolor='None',
lw=5))
if self.phase == 'updating_value_table':
self.ax[1].add_patch(
matplotlib.patches.Rectangle(self.s,
1.0,
1.0,
edgecolor='r',
facecolor='None',
lw=5))
plt.show()
print(f'Iteration: {self.i}\nState: {self.s}\n')
if self.phase == 'going_through_actions':
# Special handling for terminal states
if self.env.terminal_table[self.s_i] == 1:
self.a = 4
else:
# Get pmf over possible next states
s_prime_idxs, s_prime_probs = get_pmf_possible_s_primes(
self.env, self.s, self.a)
# Compute Q values
q_message = f'Q(s, {a_index_to_symbol[self.a]}) = '
r = self.env.reward_table[self.s_i, self.a]
for s_prime_idx, s_prime_prob in zip(s_prime_idxs,
s_prime_probs):
q_message += f'{s_prime_prob:.1f}*({r:.1f} + ' \
f'0.9*{self.value_table[s_prime_idx]:.2f}) + '
self.q_values[self.a] += s_prime_prob * (
r + 0.9 * self.value_table[s_prime_idx])
# Prepare message to be printed
q_message = q_message[:-2]
q_message += f' = {self.q_values[self.a]:.2f}'
self.q_messages += f'\n{q_message}'
if not fast_execution:
print(self.q_messages)
self.a += 1
if self.a == 4:
self.a = 3 # Show the same action as before
self.phase = 'computing_final_v'
elif self.phase == 'computing_final_v':
if self.env.terminal_table[self.s_i] == 1:
final_value_message = 'State is terminal. Setting its value to zero.\nV(s) = 0'
if not fast_execution:
print(final_value_message)
final_value = 0
else:
# Print computed Q messages
if not fast_execution:
for i, q_value in enumerate(self.q_values):
print(f'Q(s, {a_index_to_symbol[i]}) = {q_value:.2f}')
# Print resulting value of current state
final_value_message = '\nV(s) = '
final_value = 0
for q_value, pi in zip(self.q_values, self.policy[self.s_i]):
final_value_message += f'{q_value:.2f}*{pi:.1f} + '
final_value += q_value * pi
final_value_message = f'{final_value_message[:-3]}) = {final_value:.2f}'
if not fast_execution:
print(final_value_message)
# Update value table and move to next phase
self.value_table[self.s_i] = final_value
self.phase = 'updating_value_table'
elif self.phase == 'updating_value_table':
if not fast_execution:
# Print computed Q messages
for i, q_value in enumerate(self.q_values):
print(f'Q(s, {a_index_to_symbol[i]}) = {q_value:.2f}')
# Print resulting value of current state
print(f'\nV(s) = {self.value_table[self.s_i]:.2f}')
# Reset action counter, move to next state, erase previous q messages
self.a = 0
self.s_i += 1
self.q_messages = ''
self.q_values = [0, 0, 0, 0]
self.phase = 'going_through_actions'
# If reached last state, restart from first state and increment iteration number
if self.s_i == len(self.env.states):
self.s_i = 0
self.i += 1
