def test_hash(self):
from muzero.environment.action import Action
import random
for _ in range(100):
action_id = random.randint(0, 10000)
action = Action(action_id)
self.assertEqual(
action.__hash__(), action_id,
'The hash of an action should be equal to the id')
def __init__(self, environment: core.Env, number_players: int, discount: float, max_moves: int):
"""
:param environment: The gym environment to interact with
:param number_players: The number of players alternating in this environment
:param discount: The discount to apply to future rewards when calculating the value target
"""
self.environment = environment
self.action_space_size = environment.action_space.n
self.players = [Player(i) for i in range(number_players)]
self.step = 0
self.discount = discount
self.action_history = [Action(0)]
self.reward_history = [0]
self.root_values = []
self.probability_distributions = []
self.observation_history = []
self.environment = environment
self.observation_history.append(self.environment.reset())
self.child_visits = []
self.max_moves = max_moves
self.done = False
if number_players not in [1, 2]:
raise Exception('Game init', 'Valid number_player-values are: 1 or 2')
def setUp(self):
from muzero.models.dynamics_model import DynamicsModel
from muzero.environment.action import Action
import tensorflow as tf
self.dynamics_model = DynamicsModel()
self.batch_of_hidden_states = tf.ones([4, 3, 3, 1])
self.default_action = Action(0)
def legal_actions(self) -> List[Action]: """ :return: A list of all legal actions in this environment """ action_list = [] for i in range(self.action_space_size): action_list.append(Action(i)) return action_list
def get_action(self, evaluation=False):
self.rollout()
if evaluation:
return self.get_action_with_highest_visit_count()
else:
probability_distribution = self.get_probability_distribution()
return Action(
np.random.choice(a=len(probability_distribution),
size=1,
p=probability_distribution))
def test_eq(self):
from muzero.environment.action import Action
import random
action_list = [Action(action_id) for action_id in range(100)]
for _ in range(100):
action_id_one = random.randint(0, 99)
action_id_two = random.randint(0, 99)
if action_id_one != action_id_two:
self.assertNotEqual(
action_list[action_id_one], action_list[action_id_two],
"Two actions with different ids must not be equal in comparison"
)
else:
self.assertEqual(
action_list[action_id_one], action_list[action_id_two],
"Two actions with the same ids have to be equal in comparison"
)
def setUp(self):
from muzero.environment.games import Game
from muzero.environment.action import Action
from muzero.environment.player import Player
from muzero.mcts.node import Node
import gym
self.env = gym.make('CartPole-v0')
self.game = Game(environment=self.env,
discount=0.995,
number_players=1,
max_moves=50)
self.default_action = Action(0)
self.default_player = Player(0)
self.default_root_node = Node(value=1,
action=self.default_action,
hidden_state=0,
policy_logits=[0],
to_play=self.default_player,
reward=0)
# Add two child nodes for both possible action
leaf_one = Node(value=1,
action=self.default_action,
hidden_state=0,
policy_logits=[0],
to_play=self.default_player,
reward=0)
leaf_two = Node(value=1,
action=self.default_action,
hidden_state=0,
policy_logits=[0],
to_play=self.default_player,
reward=0)
leaf_one.visit_count += 1
leaf_two.visit_count += 1
self.default_root_node.child_nodes.append(leaf_one)
self.default_root_node.child_nodes.append(leaf_two)
self.default_root_node.visit_count += 3