def act(self, gs: GameState) -> int:
available_actions = gs.get_available_actions(gs.get_active_player())
state_vec = gs.get_vectorized_state()
predicted_Q_values = self.Q_action.predict(state_vec)
if np.random.random() <= self.epsilon:
chosen_action = np.random.choice(available_actions)
else:
chosen_action = available_actions[int(
np.argmax(predicted_Q_values[available_actions]))]
if self.s is not None:
target = self.r + self.gamma * predicted_Q_values[int(
np.argmax(self.Q_evaluation.predict(self.s)))]
self.Q_action.train(self.s, self.a, target)
if self.s is not None:
update_Q_evaluation = self.tau * np.array(
self.Q_action.model.get_weights()) + (1 - self.tau) * np.array(
self.Q_evaluation.model.get_weights())
self.Q_evaluation.model.set_weights(update_Q_evaluation)
self.s = state_vec
self.a = to_categorical(chosen_action, self.action_space_size)
self.r = 0.0
self.count_state += 1
return chosen_action
def act(self, gs: GameState) -> int:
gs_unique_id = gs.get_unique_id()
available_actions = gs.get_available_actions(gs.get_active_player())
state_vec = gs.get_vectorized_state()
mask_vec = np.zeros((self.action_space_size, ))
mask_vec[available_actions] = 1.0
v = self.critic.predict(state_vec)
p = self.actor.predict(state_vec, mask_vec)
indexes = np.arange(self.action_space_size)
chosen_action = np.random.choice(indexes, p=p)
# valid_actions_probability = p[available_actions]
# valid_actions_probability_sum = np.sum(valid_actions_probability)
# normalized_valid_action_probability = valid_actions_probability / valid_actions_probability_sum
# #
# chosen_action = np.random.choice(available_actions, p=normalized_valid_action_probability)
self.v.append(v)
self.s.append(state_vec)
self.m.append(mask_vec)
self.a.append(to_categorical(chosen_action, self.action_space_size))
if not self.is_last_episode_terminal:
self.r.append(self.r_temp)
self.r_temp = 0.0
self.is_last_episode_terminal = False
return chosen_action
def act(self, gs: GameState) -> int:
gs_unique_id = gs.get_unique_id()
available_actions = gs.get_available_actions(gs.get_active_player())
state_vec = gs.get_vectorized_state()
predicted_Q_values = self.Q.predict(state_vec)
if np.random.random() <= self.epsilon:
chosen_action = np.random.choice(available_actions)
else:
chosen_action = available_actions[int(
np.argmax(predicted_Q_values[available_actions]))]
if self.s is not None:
target = self.r + self.gamma * max(
predicted_Q_values[available_actions])
self.Q.train(self.s, self.a, target)
self.experience.append(
(self.s.copy(), self.a.copy(), self.r, state_vec.copy()))
print("experience", len(self.experience))
if len(self.experience) % 10 == 0:
for el in self.experience:
target = el[2] + self.gamma * el[1]
self.Q.train(el[0], el[1], target)
self.s = state_vec
self.a = to_categorical(chosen_action, self.action_space_size)
self.r = 0.0
return chosen_action
def act(self, gs: GameState) -> int:
#gs_unique_id = gs.get_unique_id()
available_actions = gs.get_available_actions(gs.get_active_player())
state_vec = gs.get_vectorized_state()
predicted_Q_values = self.Q.predict(state_vec)
if np.random.random() <= self.epsilon:
chosen_action = np.random.choice(available_actions)
else:
chosen_action = available_actions[int(
np.argmax(predicted_Q_values[available_actions]))]
if self.s is not None:
target = self.r + self.gamma * self.alternate_Q.predict(
state_vec)[available_actions][np.argmax(
self.Q.predict(state_vec)[available_actions])]
# final_target = self.model.predict(state)
# final_target[0][action] = target
# self.model.fit(state, final_target, verbose=0)
self.Q.train(self.s, self.a, target)
self.s = state_vec
self.a = to_categorical(chosen_action, self.action_space_size)
self.r = 0.0
return chosen_action
def act(self, gs: GameState) -> int:
available_actions = gs.get_available_actions(gs.get_active_player())
state_vec = gs.get_vectorized_state()
predicted_Q_values = self.Q_action.predict(state_vec)
if np.random.random() <= self.epsilon:
chosen_action = np.random.choice(available_actions)
else:
chosen_action = available_actions[int(
np.argmax(predicted_Q_values[available_actions]))]
if self.s is not None:
target = self.r + self.gamma * predicted_Q_values[int(
np.argmax(self.Q_evaluation.predict(self.s)))]
self.Q_action.train(self.s, self.a, target)
self.experience.append(
(self.s.copy(), self.a.copy(), self.r, state_vec.copy()))
if len(self.experience) % 10 == 0 and len(
self.experience) > 0 and self.epsilon > 0:
el = sample(
self.experience,
len(self.experience) if len(self.experience) < 30 else 30)
dict = {'Exp': el}
el_state = [x[0] for x in dict['Exp']]
el_a = [x[1] for x in dict['Exp']]
el_r = [x[2] for x in dict['Exp']]
el_state_plus_1 = [x[3] for x in dict['Exp']]
predicted_Q_values_list = self.Q_action.model.predict(
np.array(el_state_plus_1))
dict_predict_Q_value = {'Predict': predicted_Q_values_list}
Q_star = [
x[int(np.argmax(self.Q_evaluation.predict(el_state[i])))]
for i, x in enumerate(dict_predict_Q_value['Predict'])
]
Q_star_np = np.array(Q_star)
target = np.array(el_r) + self.gamma * Q_star_np
self.Q_action.retrain(np.array(el_state), np.array(el_a), target)
if self.s is not None:
update_Q_evaluation = self.tau * np.array(
self.Q_action.model.get_weights()) + (1 - self.tau) * np.array(
self.Q_evaluation.model.get_weights())
self.Q_evaluation.model.set_weights(update_Q_evaluation)
self.s = state_vec
self.a = to_categorical(chosen_action, self.action_space_size)
self.r = 0.0
self.count_state += 1
return chosen_action
def act(self, gs: GameState) -> int:
self.priority.append(0.001)
self.memory.append((self.s, self.s, self.a, self.r, True))
available_actions = gs.get_available_actions(gs.get_active_player())
state_vec = gs.get_vectorized_state()
predicted_Q_values = self.Q.predict(state_vec)
if np.random.random() <= self.epsilon:
chosen_action = np.random.choice(available_actions)
else:
chosen_action = available_actions[int(
np.argmax(predicted_Q_values[available_actions]))]
batch, importance = self.get_priority_experience_batch()
for b, i in zip(batch, importance):
state, next_state, action, reward, done = b
target = reward
if not done:
if self.s is not None:
# target = target + self.gamma * self.alternate_Q.predict(state_vec)[available_actions][
# np.argmax(self.Q.predict(state_vec)[available_actions])]
# self.Q.train(self.s, self.a, target)
q_next = reward + self.gamma * self.alternate_Q.predict(
next_state)[available_actions][np.argmax(
self.Q.predict(next_state)[available_actions])]
target = q_next
q = self.alternate_Q.predict(
next_state)[available_actions][np.argmax(
self.Q.predict(next_state)[available_actions])]
p = (np.abs(q_next - q) + (np.e**-10))**self.alpha
self.priority.append(p)
self.memory.append(
(state, next_state, action, reward, done))
self.Q.train(self.s, self.a, target)
imp = i**(1 - self.epsilon)
imp = np.reshape(imp, 1)
# self.remember(self.s, state_vec, self.a, self.r, True)
# batch = random.choices(self.memory, k=self.batch_size)
# for state, next_state, action, reward, done in batch:
# target = reward
# if not done:
# if self.s is not None:
# target = target + self.gamma * self.alternate_Q.predict(state_vec)[available_actions][
# np.argmax(self.Q.predict(state_vec)[available_actions])]
# self.Q.train(self.s, self.a, target)
# self.remember(self.s, state_vec, self.a, self.r, True)
self.s = state_vec
self.a = to_categorical(chosen_action, self.action_space_size)
self.r = 0.0
return chosen_action
def act(self, gs: GameState) -> int:
available_actions = gs.get_available_actions(gs.get_active_player())
state_vec = gs.get_vectorized_state()
predicted_Q_values = self.Q.predict(state_vec)
if np.random.random() <= self.epsilon:
chosen_action = np.random.choice(available_actions)
else:
chosen_action = available_actions[int(
np.argmax(predicted_Q_values[available_actions]))]
if self.s is not None:
target = self.r + self.gamma * max(
predicted_Q_values[available_actions])
self.Q.train(self.s, self.a, target)
self.s = state_vec
self.a = to_categorical(chosen_action, self.action_space_size)
self.r = 0.0
return chosen_action
def act(self, gs: GameState) -> int:
available_actions = gs.get_available_actions(gs.get_active_player())
state_vec = gs.get_vectorized_state()
action_probs = self.Q_policy_function.predict(state_vec)
chosen_action = np.random.choice(available_actions,
p=action_probs,
replace=True)
self.state.append(state_vec)
self.rewards.append(self.r)
self.log_probs.append(np.log(action_probs))
self.probs.append(action_probs)
self.action.append(chosen_action)
self.a.append(to_categorical(chosen_action, self.action_space_size))
self.r = 0.0
return chosen_action
def act(self, gs: GameState) -> int:
available_actions = gs.get_available_actions(gs.get_active_player())
state_vec = gs.get_vectorized_state()
predicted_Q_values = self.Q.predict(state_vec)
if np.random.random() <= self.epsilon:
chosen_action = np.random.choice(available_actions)
else:
chosen_action = available_actions[int(
np.argmax(predicted_Q_values[available_actions]))]
batch = random.choices(self.memory, k=self.batch_size)
for state, next_state, action, reward, done in batch:
target = reward
if not done:
if self.s is not None:
target = target + self.gamma * self.alternate_Q.predict(
state_vec)[available_actions][np.argmax(
self.Q.predict(state_vec)[available_actions])]
self.Q.train(self.s, self.a, target)
self.remember(self.s, state_vec, self.a, self.r, True)
self.s = state_vec
self.a = to_categorical(chosen_action, self.action_space_size)
self.r = 0.0
return chosen_action
def act(self, gs: GameState) -> int:
if self.apprentice_training_count > self.apprentice_training_before_takeover:
return gs.get_available_actions(gs.get_active_player())[np.argmax(
self.brain.predict(np.array([
gs.get_vectorized_state()
]))[0][gs.get_available_actions(gs.get_active_player())])]
root_hash = gs.get_unique_id()
memory = self.memory if self.keep_memory else dict()
if root_hash not in memory:
ExpertApprenticeAgent.create_node_in_memory(
memory, root_hash,
gs.get_available_actions(gs.get_active_player()),
gs.get_active_player())
for i in range(self.max_iteration):
gs_copy = gs.clone()
s = gs_copy.get_unique_id()
history = []
# SELECTION
while not gs_copy.is_game_over() and all(
(edge['n'] > 0 for edge in memory[s])):
chosen_edge = max(((edge, ExpertApprenticeAgent.ucb_1(edge))
for edge in memory[s]),
key=lambda kv: kv[1])[0]
history.append((s, chosen_edge))
gs_copy.step(gs_copy.get_active_player(), chosen_edge['a'])
s = gs_copy.get_unique_id()
if s not in memory:
ExpertApprenticeAgent.create_node_in_memory(
memory, s,
gs_copy.get_available_actions(
gs_copy.get_active_player()),
gs_copy.get_active_player())
# EXPANSION
if not gs_copy.is_game_over():
chosen_edge = choice(
list(
filter(lambda e: e['n'] == 0,
(edge for edge in memory[s]))))
history.append((s, chosen_edge))
gs_copy.step(gs_copy.get_active_player(), chosen_edge['a'])
s = gs_copy.get_unique_id()
if s not in memory:
ExpertApprenticeAgent.create_node_in_memory(
memory, s,
gs_copy.get_available_actions(
gs_copy.get_active_player()),
gs_copy.get_active_player())
# SIMULATION
while not gs_copy.is_game_over():
gs_copy.step(
gs_copy.get_active_player(),
choice(
gs_copy.get_available_actions(
gs_copy.get_active_player())))
scores = gs_copy.get_scores()
# REMONTEE DU SCORE
for (s, edge) in history:
edge['n'] += 1
edge['r'] += scores[edge['p']]
for neighbour_edge in memory[s]:
neighbour_edge['np'] += 1
target = np.zeros(gs.get_action_space_size())
for edge in memory[root_hash]:
target[edge['a']] = edge['n']
target /= np.sum(target)
self.states_buffer.append(gs.get_vectorized_state())
self.actions_buffer.append(target)
if len(self.states_buffer) > 200:
self.apprentice_training_count += 1
self.brain.fit(np.array(self.states_buffer),
np.array(self.actions_buffer))
self.states_buffer.clear()
self.actions_buffer.clear()
if self.apprentice_training_count > self.apprentice_training_before_takeover:
print('Apprentice is playing next round')
return max((edge for edge in memory[root_hash]),
key=lambda e: e['n'])['a']
def act(self, gs: GameState) -> int:
root_hash = gs.get_unique_id()
memory = self.memory if self.keep_memory else dict()
if root_hash not in memory:
q_values = self.brain.predict(gs.get_vectorized_state())
HalfAlphaZeroAgent.create_node_in_memory(
memory, root_hash,
gs.get_available_actions(gs.get_active_player()),
gs.get_active_player(), q_values)
for i in range(self.max_iteration):
gs_copy = gs.clone()
s = gs_copy.get_unique_id()
history = []
# SELECTION
while not gs_copy.is_game_over() and all(
(edge['n'] > 0 for edge in memory[s])):
chosen_edge = max(((edge, HalfAlphaZeroAgent.ucb_1(edge))
for edge in memory[s]),
key=lambda kv: kv[1])[0]
history.append((s, chosen_edge))
gs_copy.step(gs_copy.get_active_player(), chosen_edge['a'])
s = gs_copy.get_unique_id()
if s not in memory:
q_values = self.brain.predict(
gs_copy.get_vectorized_state())
HalfAlphaZeroAgent.create_node_in_memory(
memory, s,
gs_copy.get_available_actions(
gs_copy.get_active_player()),
gs_copy.get_active_player(), q_values)
# EXPANSION
if not gs_copy.is_game_over():
chosen_edge = choice(
list(
filter(lambda e: e['n'] == 0,
(edge for edge in memory[s]))))
history.append((s, chosen_edge))
gs_copy.step(gs_copy.get_active_player(), chosen_edge['a'])
s = gs_copy.get_unique_id()
if s not in memory:
q_values = self.brain.predict(
gs_copy.get_vectorized_state())
HalfAlphaZeroAgent.create_node_in_memory(
memory, s,
gs_copy.get_available_actions(
gs_copy.get_active_player()),
gs_copy.get_active_player(), q_values)
scores = np.zeros(gs_copy.player_count())
scores_set = np.zeros(gs_copy.player_count())
# REMONTEE DU SCORE
for (s, edge) in history:
if scores_set[edge['p']] == 0:
scores_set[edge['p']] = 1.0
scores[edge['p']] = edge['q']
edge['n'] += 1
edge['r'] += scores[edge['p']]
for neighbour_edge in memory[s]:
neighbour_edge['np'] += 1
chosen_action = max((edge for edge in memory[root_hash]),
key=lambda e: e['n'])['a']
if len(self.states_buffer) > 0:
self.rewards_buffer.append(self.intermediate_reward)
self.states_buffer.append(gs.get_vectorized_state())
self.actions_buffer.append(
to_categorical(chosen_action, gs.get_action_space_size()))
self.intermediate_reward = 0.0
return chosen_action