def play_game(config: MuZeroConfig,
network: AbstractNetwork,
train: bool = True) -> AbstractGame:
"""
Each game is produced by starting at the initial board position, then
repeatedly executing a Monte Carlo Tree Search to generate moves until the end
of the game is reached.
"""
game = config.new_game()
mode_action_select = 'softmax' if train else 'max'
while not game.terminal() and len(game.history) < config.max_moves:
# At the root of the search tree we use the representation function to
# obtain a hidden state given the current observation.
root = Node(0)
current_observation = game.make_image(-1)
expand_node(root, game.to_play(), game.legal_actions(),
network.initial_inference(current_observation))
if train:
add_exploration_noise(config, root)
# We then run a Monte Carlo Tree Search using only action sequences and the
# model learned by the networks.
run_mcts(config, root, game.action_history(), network)
action = select_action(config,
len(game.history),
root,
network,
mode=mode_action_select)
game.apply(action)
game.store_search_statistics(root)
return game
def play_game(config: MuZeroConfig,
storage: SharedStorage,
train: bool = True,
visual: bool = False,
queue: Queue = None) -> AbstractGame:
"""
Each game is produced by starting at the initial board position, then
repeatedly executing a Monte Carlo Tree Search to generate moves until the end
of the game is reached.
"""
if queue:
network = storage.latest_network_for_process()
else:
network = storage.current_network
start = time()
game = config.new_game()
mode_action_select = 'softmax' if train else 'max'
while not game.terminal() and len(game.history) < config.max_moves:
# At the root of the search tree we use the representation function to
# obtain a hidden state given the current observation.
root = Node(0)
current_observation = game.make_image(-1)
expand_node(root, game.to_play(), game.legal_actions(),
network.initial_inference(current_observation))
if train:
add_exploration_noise(config, root)
# We then run a Monte Carlo Tree Search using only action sequences and the
# model learned by the networks.
run_mcts(config, root, game.action_history(), network)
action = select_action(config,
len(game.history),
root,
network,
mode=mode_action_select)
game.apply(action)
game.store_search_statistics(root)
if visual:
game.env.render()
if visual:
if game.terminal():
print('Model lost game')
else:
print('Exceeded max moves')
game.env.close()
if queue:
queue.put(game)
print("Finished game episode after " + str(time() - start) +
" seconds. Exceeded max moves? " + str(not game.terminal()))
print("Score: ", sum(game.rewards))
return game
def play_game(config: MuZeroConfig, network: AbstractNetwork, train: bool = True) -> AbstractGame:
game = config.new_game()
mode_action_select = 'softmax' if train else 'max'
while not game.terminal() and len(game.history) < config.max_moves:
root = Node(0)
current_observation = game.make_image(-1)
expand_node(root, game.to_play(), game.legal_actions(), network.initial_inference(current_observation))
if train:
add_exploration_noise(config, root)
run_mcts(config, root, game.action_history(), network)
action = select_action(config, len(game.history), root, network, mode=mode_action_select)
game.apply(action)
game.store_search_statistics(root)
return game