def create_mock_vector_steps(specs, num_agents=1, number_visual_observations=0): """ Creates a mock BatchedStepResult with vector observations. Imitates constant vector observations, rewards, dones, and agents. :BehaviorSpecs specs: The BehaviorSpecs for this mock :int num_agents: Number of "agents" to imitate in your BatchedStepResult values. """ obs = [ np.array([num_agents * [1, 2, 3]], dtype=np.float32).reshape(num_agents, 3) ] if number_visual_observations: obs += [np.zeros(shape=(num_agents, 8, 8, 3), dtype=np.float32) ] * number_visual_observations rewards = np.array(num_agents * [1.0]) agents = np.array(range(0, num_agents)) group_id = np.array(num_agents * [0]) group_rewards = np.array(num_agents * [0.0]) return ( DecisionSteps(obs, rewards, agents, None, group_id, group_rewards), TerminalSteps.empty(specs), )
def _make_batched_step( self, name: str, done: bool, reward: float) -> Tuple[DecisionSteps, TerminalSteps]: m_vector_obs = self._make_obs(self.goal[name]) m_reward = np.array([reward], dtype=np.float32) m_agent_id = np.array([self.agent_id[name]], dtype=np.int32) action_mask = self._generate_mask() decision_step = DecisionSteps(m_vector_obs, m_reward, m_agent_id, action_mask) terminal_step = TerminalSteps.empty(self.behavior_spec) if done: self._reset_agent(name) new_vector_obs = self._make_obs(self.goal[name]) ( new_reward, new_done, new_agent_id, new_action_mask, ) = self._construct_reset_step(name) decision_step = DecisionSteps(new_vector_obs, new_reward, new_agent_id, new_action_mask) terminal_step = TerminalSteps(m_vector_obs, m_reward, np.array([False], dtype=np.bool), m_agent_id) return (decision_step, terminal_step)
def _make_batched_step( self, name: str, done: bool, reward: float) -> Tuple[DecisionSteps, TerminalSteps]: recurrent_obs_val = (self.goal[name] if self.step_count[name] <= self.num_show_steps else 0) m_vector_obs = self._make_obs(recurrent_obs_val) m_reward = np.array([reward], dtype=np.float32) m_agent_id = np.array([self.agent_id[name]], dtype=np.int32) action_mask = self._generate_mask() decision_step = DecisionSteps(m_vector_obs, m_reward, m_agent_id, action_mask) terminal_step = TerminalSteps.empty(self.behavior_spec) if done: self.final_rewards[name].append(self.rewards[name]) self._reset_agent(name) recurrent_obs_val = (self.goal[name] if self.step_count[name] <= self.num_show_steps else 0) new_vector_obs = self._make_obs(recurrent_obs_val) ( new_reward, new_done, new_agent_id, new_action_mask, ) = self._construct_reset_step(name) decision_step = DecisionSteps(new_vector_obs, new_reward, new_agent_id, new_action_mask) terminal_step = TerminalSteps(m_vector_obs, m_reward, np.array([False], dtype=np.bool), m_agent_id) return (decision_step, terminal_step)
def test_empty_terminal_steps(): specs = BehaviorSpec(observation_shapes=[(3, 2), (5, )], action_spec=ActionSpec.create_continuous(3)) ts = TerminalSteps.empty(specs) assert len(ts.obs) == 2 assert ts.obs[0].shape == (0, 3, 2) assert ts.obs[1].shape == (0, 5)
def test_empty_terminal_steps(): specs = BehaviorSpec( sensor_specs=create_sensor_specs_with_shapes([(3, 2), (5, )]), action_spec=ActionSpec.create_continuous(3), ) ts = TerminalSteps.empty(specs) assert len(ts.obs) == 2 assert ts.obs[0].shape == (0, 3, 2) assert ts.obs[1].shape == (0, 5)
def test_empty_terminal_steps(): specs = BehaviorSpec( observation_shapes=[(3, 2), (5, )], action_type=ActionType.CONTINUOUS, action_shape=3, ) ts = TerminalSteps.empty(specs) assert len(ts.obs) == 2 assert ts.obs[0].shape == (0, 3, 2) assert ts.obs[1].shape == (0, 5)
def create_mock_steps( num_agents: int = 1, num_vector_observations: int = 0, num_vis_observations: int = 0, action_shape: List[int] = None, discrete: bool = False, done: bool = False, ) -> Tuple[DecisionSteps, TerminalSteps]: """ Creates a mock Tuple[DecisionSteps, TerminalSteps] with observations. Imitates constant vector/visual observations, rewards, dones, and agents. :int num_agents: Number of "agents" to imitate. :int num_vector_observations: Number of "observations" in your observation space :int num_vis_observations: Number of "observations" in your observation space :int num_vector_acts: Number of actions in your action space :bool discrete: Whether or not action space is discrete :bool done: Whether all the agents in the batch are done """ if action_shape is None: action_shape = [2] obs_list = [] for _ in range(num_vis_observations): obs_list.append(np.ones((num_agents, 84, 84, 3), dtype=np.float32)) if num_vector_observations > 1: obs_list.append( np.array(num_agents * [num_vector_observations * [1]], dtype=np.float32)) action_mask = None if discrete: action_mask = [ np.array(num_agents * [action_size * [False]]) for action_size in action_shape ] reward = np.array(num_agents * [1.0], dtype=np.float32) interrupted = np.array(num_agents * [False], dtype=np.bool) agent_id = np.arange(num_agents, dtype=np.int32) behavior_spec = BehaviorSpec( [(84, 84, 3)] * num_vis_observations + [(num_vector_observations, 0, 0)], ActionType.DISCRETE if discrete else ActionType.CONTINUOUS, action_shape if discrete else action_shape[0], ) if done: return ( DecisionSteps.empty(behavior_spec), TerminalSteps(obs_list, reward, interrupted, agent_id), ) else: return ( DecisionSteps(obs_list, reward, agent_id, action_mask), TerminalSteps.empty(behavior_spec), )
def create_mock_steps( num_agents: int, observation_shapes: List[Tuple], action_shape: Union[int, Tuple[int]] = None, discrete: bool = False, done: bool = False, ) -> Tuple[DecisionSteps, TerminalSteps]: """ Creates a mock Tuple[DecisionSteps, TerminalSteps] with observations. Imitates constant vector/visual observations, rewards, dones, and agents. :int num_agents: Number of "agents" to imitate. :List observation_shapes: A List of the observation spaces in your steps :int num_vector_acts: Number of actions in your action space :bool discrete: Whether or not action space is discrete :bool done: Whether all the agents in the batch are done """ if action_shape is None: action_shape = 2 obs_list = [] for _shape in observation_shapes: obs_list.append(np.ones((num_agents, ) + _shape, dtype=np.float32)) action_mask = None if discrete and isinstance(action_shape, Iterable): action_mask = [ np.array(num_agents * [action_size * [False]]) for action_size in action_shape # type: ignore ] # type: ignore reward = np.array(num_agents * [1.0], dtype=np.float32) interrupted = np.array(num_agents * [False], dtype=np.bool) agent_id = np.arange(num_agents, dtype=np.int32) behavior_spec = BehaviorSpec( observation_shapes, ActionType.DISCRETE if discrete else ActionType.CONTINUOUS, action_shape, ) if done: return ( DecisionSteps.empty(behavior_spec), TerminalSteps(obs_list, reward, interrupted, agent_id), ) else: return ( DecisionSteps(obs_list, reward, agent_id, action_mask), TerminalSteps.empty(behavior_spec), )
def _update_state(self, output: UnityRLOutputProto) -> None: """ Collects experience information from all external brains in environment at current step. """ for brain_name in self._env_specs.keys(): if brain_name in output.agentInfos: agent_info_list = output.agentInfos[brain_name].value self._env_state[brain_name] = steps_from_proto( agent_info_list, self._env_specs[brain_name] ) else: self._env_state[brain_name] = ( DecisionSteps.empty(self._env_specs[brain_name]), TerminalSteps.empty(self._env_specs[brain_name]), ) self._side_channel_manager.process_side_channel_message(output.side_channel)
def create_mock_steps( num_agents: int, observation_specs: List[ObservationSpec], action_spec: ActionSpec, done: bool = False, grouped: bool = False, ) -> Tuple[DecisionSteps, TerminalSteps]: """ Creates a mock Tuple[DecisionSteps, TerminalSteps] with observations. Imitates constant vector/visual observations, rewards, dones, and agents. :int num_agents: Number of "agents" to imitate. :List observation_specs: A List of the observation specs in your steps :int action_spec: ActionSpec for the agent :bool done: Whether all the agents in the batch are done """ obs_list = [] for obs_spec in observation_specs: obs_list.append(np.ones((num_agents,) + obs_spec.shape, dtype=np.float32)) action_mask = None if action_spec.is_discrete(): action_mask = [ np.array(num_agents * [action_size * [False]]) for action_size in action_spec.discrete_branches # type: ignore ] # type: ignore reward = np.array(num_agents * [1.0], dtype=np.float32) interrupted = np.array(num_agents * [False], dtype=np.bool) agent_id = np.arange(num_agents, dtype=np.int32) _gid = 1 if grouped else 0 group_id = np.array(num_agents * [_gid], dtype=np.int32) group_reward = np.array(num_agents * [0.0], dtype=np.float32) behavior_spec = BehaviorSpec(observation_specs, action_spec) if done: return ( DecisionSteps.empty(behavior_spec), TerminalSteps( obs_list, reward, interrupted, agent_id, group_id, group_reward ), ) else: return ( DecisionSteps( obs_list, reward, agent_id, action_mask, group_id, group_reward ), TerminalSteps.empty(behavior_spec), )
def get_steps(self, behavior_name): # This gets the individual DecisionSteps and TerminalSteps # from the envs and merges them into a batch to be sent # to the AgentProcessor. dec_vec_obs = [] dec_reward = [] dec_group_reward = [] dec_agent_id = [] dec_group_id = [] ter_vec_obs = [] ter_reward = [] ter_group_reward = [] ter_agent_id = [] ter_group_id = [] interrupted = [] action_mask = None terminal_step = TerminalSteps.empty(self.behavior_spec) decision_step = None for i in range(self.num_agents): name_and_num = behavior_name + str(i) env = self.envs[name_and_num] _dec, _term = env.step_result[behavior_name] if not self.dones[name_and_num]: dec_agent_id.append(i) dec_group_id.append(1) if len(dec_vec_obs) > 0: for j, obs in enumerate(_dec.obs): dec_vec_obs[j] = np.concatenate((dec_vec_obs[j], obs), axis=0) else: for obs in _dec.obs: dec_vec_obs.append(obs) dec_reward.append(_dec.reward[0]) dec_group_reward.append(_dec.group_reward[0]) if _dec.action_mask is not None: if action_mask is None: action_mask = [] if len(action_mask) > 0: action_mask[0] = np.concatenate( (action_mask[0], _dec.action_mask[0]), axis=0) else: action_mask.append(_dec.action_mask[0]) if len(_term.reward) > 0 and name_and_num in self.just_died: ter_agent_id.append(i) ter_group_id.append(1) if len(ter_vec_obs) > 0: for j, obs in enumerate(_term.obs): ter_vec_obs[j] = np.concatenate((ter_vec_obs[j], obs), axis=0) else: for obs in _term.obs: ter_vec_obs.append(obs) ter_reward.append(_term.reward[0]) ter_group_reward.append(_term.group_reward[0]) interrupted.append(False) self.just_died.remove(name_and_num) decision_step = DecisionSteps( dec_vec_obs, dec_reward, dec_agent_id, action_mask, dec_group_id, dec_group_reward, ) terminal_step = TerminalSteps( ter_vec_obs, ter_reward, interrupted, ter_agent_id, ter_group_id, ter_group_reward, ) return (decision_step, terminal_step)