def action_probabilities(self, state, player_id=None): cur_player = state.current_player() if player_id is None else player_id legal_actions = state.legal_actions(cur_player) step_type = rl_environment.StepType.LAST if state.is_terminal( ) else rl_environment.StepType.MID self._obs["current_player"] = cur_player self._obs["info_state"][cur_player] = ( state.information_state_tensor(cur_player)) self._obs["legal_actions"][cur_player] = legal_actions # pylint: disable=protected-access rewards = state.rewards() if rewards: time_step = rl_environment.TimeStep( observations=self._obs, rewards=rewards, discounts=self._env._discounts, step_type=step_type) else: rewards = [0] * self._num_players time_step = rl_environment.TimeStep( observations=self._obs, rewards=rewards, discounts=self._env._discounts, step_type=rl_environment.StepType.FIRST) # pylint: enable=protected-access p = self._policy.step(time_step, is_evaluation=True).probs prob_dict = {action: p[action] for action in legal_actions} return prob_dict
def step(self, actions): """Updates the environment according to `actions` and returns a `TimeStep`. Args: actions: A singleton list with an integer, or an integer, representing the action the agent took. Returns: A `rl_environment.TimeStep` namedtuple containing: observation: singleton list of dicts containing player observations, each corresponding to `observation_spec()`. reward: singleton list containing the reward at this timestep, or None if step_type is `rl_environment.StepType.FIRST`. discount: singleton list containing the discount in the range [0, 1], or None if step_type is `rl_environment.StepType.FIRST`. step_type: A `rl_environment.StepType` value. """ if self._should_reset: return self.reset() self._time_counter += 1 if isinstance(actions, list): action = actions[0] elif isinstance(actions, six.integer_types): action = actions else: raise ValueError("Action not supported.", actions) dx = 0 dy = 0 if action == LEFT: dx -= 1 elif action == RIGHT: dx += 1 if action == UP: dy -= 1 elif action == DOWN: dy += 1 self._state += np.array([dy, dx]) self._state = self._state.clip(0, [self._height - 1, self._width - 1]) done = self._is_pit(self._state) or self._is_goal(self._state) done = done or self._time_counter >= self._max_t # Return observation step_type = (rl_environment.StepType.LAST if done else rl_environment.StepType.MID) self._should_reset = step_type == rl_environment.StepType.LAST observations = { "info_state": [self._state.copy()], "legal_actions": [self._legal_actions], "current_player": 0, } return rl_environment.TimeStep(observations=observations, rewards=[self._get_reward(self._state)], discounts=self._discounts, step_type=step_type)
def action_probabilities(self, state, player_id=None): if state.is_simultaneous_node(): assert player_id is not None, "Player ID should be specified." else: if player_id is None: player_id = state.current_player() else: assert player_id == state.current_player() legal_actions = state.legal_actions(player_id) self._obs["current_player"] = player_id self._obs["info_state"][player_id] = ( state.observation_tensor(player_id) if self._use_observation else state.information_state_tensor(player_id)) self._obs["legal_actions"][player_id] = legal_actions info_state = rl_environment.TimeStep(observations=self._obs, rewards=None, discounts=None, step_type=None) p = self._agents[player_id].step(info_state, is_evaluation=True).probs prob_dict = {action: p[action] for action in legal_actions} return prob_dict
def action_probabilities(self, state, player_id=None): cur_player = state.current_player() legal_actions = state.legal_actions(cur_player) self._obs["current_player"] = cur_player self._obs["info_state"][cur_player] = ( state.information_state_tensor(cur_player)) self._obs["legal_actions"][cur_player] = legal_actions info_state = rl_environment.TimeStep( observations=self._obs, rewards=None, discounts=None, step_type=None) p = self._policies[cur_player].step(info_state, is_evaluation=True).probs prob_dict = {action: p[action] for action in legal_actions} return prob_dict
def reset(self): """Resets the environment.""" self._should_reset = False self._time_counter = 0 self._state = np.array([self._height - 1, 0]) observations = { "info_state": [self._state.copy()], "legal_actions": [self._legal_actions], "current_player": 0, } return rl_environment.TimeStep(observations=observations, rewards=None, discounts=None, step_type=rl_environment.StepType.FIRST)
def test_step(self): agent = minimal_agent.MinimalAgent(player_id=0, num_actions=10) legal_actions = [0, 2, 3, 5] time_step = rl_environment.TimeStep( observations={ "info_state": [[0], [1]], "legal_actions": [legal_actions, []], "current_player": 0 }, rewards=None, discounts=None, step_type=None) agent_output = agent.step(time_step) self.assertIn(agent_output.action, legal_actions) self.assertAlmostEqual(sum(agent_output.probs), 1.0) self.assertEqual(agent_output.action, 0)
def test_step(self): agent = random_agent.RandomAgent(player_id=0, num_actions=10) legal_actions = [0, 2, 3, 5] time_step = rl_environment.TimeStep( observations={ "info_state": [[0], [1]], "legal_actions": [legal_actions, []], "current_player": 0 }, rewards=None, discounts=None, step_type=None) agent_output = agent.step(time_step) self.assertIn(agent_output.action, legal_actions) self.assertAlmostEqual(sum(agent_output.probs), 1.0) self.assertEqual( len([x for x in agent_output.probs if x > 0]), len(legal_actions)) self.assertTrue( np.allclose(agent_output.probs[legal_actions], [.25] * 4, atol=1e-5))
def reset(self): """Resets the environment.""" self._should_reset = False self._ball_pos = _Point(x=self._rng.randint(0, self._width - 1), y=0) self._paddle_pos = _Point(x=self._rng.randint(0, self._width - 1), y=self._height - 1) legal_actions = [NOOP] if self._paddle_pos.x > 0: legal_actions.append(LEFT) if self._paddle_pos.x < self._width - 1: legal_actions.append(RIGHT) observations = { "info_state": [self._get_observation()], "legal_actions": [legal_actions], "current_player": 0, } return rl_environment.TimeStep(observations=observations, rewards=None, discounts=None, step_type=rl_environment.StepType.FIRST)
def step(self, actions): """Updates the environment according to `actions` and returns a `TimeStep`. Args: actions: A singleton list with an integer, or an integer, representing the action the agent took. Returns: A `rl_environment.TimeStep` namedtuple containing: observation: singleton list of dicts containing player observations, each corresponding to `observation_spec()`. reward: singleton list containing the reward at this timestep, or None if step_type is `rl_environment.StepType.FIRST`. discount: singleton list containing the discount in the range [0, 1], or None if step_type is `rl_environment.StepType.FIRST`. step_type: A `rl_environment.StepType` value. """ if self._should_reset: return self.reset() if isinstance(actions, list): action = actions[0] elif isinstance(actions, six.integer_types): action = actions else: raise ValueError("Action not supported.", actions) # Update paddle position x, y = self._paddle_pos.x, self._paddle_pos.y if action == LEFT: x -= 1 elif action == RIGHT: x += 1 elif action != NOOP: raise ValueError("unrecognized action ", action) assert 0 <= x < self._width, ( "Illegal action detected ({}), new state: ({},{})".format( action, x, y)) self._paddle_pos = _Point(x, y) # Update ball position x, y = self._ball_pos.x, self._ball_pos.y if y == self._height - 1: done = True reward = 1.0 if x == self._paddle_pos.x else -1.0 else: done = False y += 1 reward = 0.0 self._ball_pos = _Point(x, y) # Return observation step_type = (rl_environment.StepType.LAST if done else rl_environment.StepType.MID) self._should_reset = step_type == rl_environment.StepType.LAST legal_actions = [NOOP] if self._paddle_pos.x > 0: legal_actions.append(LEFT) if self._paddle_pos.x < self._width - 1: legal_actions.append(RIGHT) observations = { "info_state": [self._get_observation()], "legal_actions": [legal_actions], "current_player": 0, } return rl_environment.TimeStep(observations=observations, rewards=[reward], discounts=self._discounts, step_type=step_type)
def test_step(self): player_id = 3 agent = higher_agent.HigherAgent(player_id=player_id, num_actions=52) # Vul: None # S A # H K964 # D 864 # C 96432 # S T7542 S K98 # H T3 H J82 # D AQ952 D J7 # C A C QJT87 # S QJ63 # H AQ75 # D KT3 # C K5 # West North East South # Pass Pass 1N # Pass Pass Pass # We lead with D2. time_step = rl_environment.TimeStep( observations={ 'info_state': [ [ 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 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step_type=None) agent_output = agent.step(time_step) self.assertIn(agent_output.action, time_step.observations["legal_actions"][player_id]) self.assertAlmostEqual(sum(agent_output.probs), 1.0) self.assertEqual(agent_output.action, 1)