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)
p = np.array(p)
p /= p.sum()
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:
available_actions = gs.get_available_actions(gs.get_active_player())
state_vec = gs.get_vectorized_state(
mode="2D" if self.using_convolution else None)
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:
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
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), verbose=0
)
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"]