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
