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': [
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1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0,
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0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0,
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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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],
[
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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,
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],
'legal_actions':
[[], [], [], [1, 3, 6, 11, 13, 15, 23, 29, 34, 35, 41, 48,
49]],
'current_player':
3
},
rewards=[0.0, 0.0, 0.0, 0.0],
discounts=[1.0, 1.0, 1.0, 1.0],
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)
