import logging import numpy as np import io from mlagents.envs.communicator_objects.agent_info_pb2 import AgentInfoProto from mlagents.envs.communicator_objects.brain_parameters_pb2 import BrainParametersProto from mlagents.envs.communicator_objects.observation_pb2 import ObservationProto from mlagents.envs.timers import hierarchical_timer, timed from typing import Dict, List, NamedTuple, Optional from PIL import Image logger = logging.getLogger("mlagents.envs") class CameraResolution(NamedTuple): height: int width: int num_channels: int @property def gray_scale(self) -> bool: return self.num_channels == 1 def __str__(self): return f"CameraResolution({self.height}, {self.width}, {self.num_channels})" class BrainParameters: def __init__( self, brain_name: str, vector_observation_space_size: int, camera_resolutions: List[CameraResolution], vector_action_space_size: List[int], vector_action_descriptions: List[str], vector_action_space_type: int, ): """ Contains all brain-specific parameters. """ self.brain_name = brain_name self.vector_observation_space_size = vector_observation_space_size self.number_visual_observations = len(camera_resolutions) self.camera_resolutions = camera_resolutions self.vector_action_space_size = vector_action_space_size self.vector_action_descriptions = vector_action_descriptions self.vector_action_space_type = ["discrete", "continuous"][ vector_action_space_type ] def __str__(self): return """Unity brain name: {} Number of Visual Observations (per agent): {} Camera Resolutions: {} Vector Observation space size (per agent): {} Vector Action space type: {} Vector Action space size (per agent): {} Vector Action descriptions: {}""".format( self.brain_name, str(self.number_visual_observations), str([str(cr) for cr in self.camera_resolutions]), str(self.vector_observation_space_size), self.vector_action_space_type, str(self.vector_action_space_size), ", ".join(self.vector_action_descriptions), ) @staticmethod def from_proto( brain_param_proto: BrainParametersProto, agent_info: AgentInfoProto ) -> "BrainParameters": """ Converts brain parameter proto to BrainParameter object. :param brain_param_proto: protobuf object. :return: BrainParameter object. """ resolutions = [ CameraResolution(obs.shape[0], obs.shape[1], obs.shape[2]) for obs in agent_info.observations if len(obs.shape) >= 3 ] total_vector_obs = sum( obs.shape[0] for obs in agent_info.observations if len(obs.shape) == 1 ) brain_params = BrainParameters( brain_name=brain_param_proto.brain_name, vector_observation_space_size=total_vector_obs, camera_resolutions=resolutions, vector_action_space_size=list(brain_param_proto.vector_action_size), vector_action_descriptions=list( brain_param_proto.vector_action_descriptions ), vector_action_space_type=brain_param_proto.vector_action_space_type, ) return brain_params class BrainInfo: def __init__( self, visual_observation, vector_observation, reward=None, agents=None, local_done=None, vector_action=None, max_reached=None, action_mask=None, ): """ Describes experience at current step of all agents linked to a brain. """ self.visual_observations = visual_observation self.vector_observations = vector_observation self.rewards = reward self.local_done = local_done self.max_reached = max_reached self.agents = agents self.previous_vector_actions = vector_action self.action_masks = action_mask @staticmethod def merge_memories(m1, m2, agents1, agents2): if len(m1) == 0 and len(m2) != 0: m1 = np.zeros((len(agents1), m2.shape[1])) elif len(m2) == 0 and len(m1) != 0: m2 = np.zeros((len(agents2), m1.shape[1])) elif m2.shape[1] > m1.shape[1]: new_m1 = np.zeros((m1.shape[0], m2.shape[1])) new_m1[0 : m1.shape[0], 0 : m1.shape[1]] = m1 return np.append(new_m1, m2, axis=0) elif m1.shape[1] > m2.shape[1]: new_m2 = np.zeros((m2.shape[0], m1.shape[1])) new_m2[0 : m2.shape[0], 0 : m2.shape[1]] = m2 return np.append(m1, new_m2, axis=0) return np.append(m1, m2, axis=0) @staticmethod @timed def process_pixels(image_bytes: bytes, gray_scale: bool) -> np.ndarray: """ Converts byte array observation image into numpy array, re-sizes it, and optionally converts it to grey scale :param gray_scale: Whether to convert the image to grayscale. :param image_bytes: input byte array corresponding to image :return: processed numpy array of observation from environment """ with hierarchical_timer("image_decompress"): image_bytearray = bytearray(image_bytes) image = Image.open(io.BytesIO(image_bytearray)) # Normally Image loads lazily, this forces it to do loading in the timer scope. image.load() s = np.array(image) / 255.0 if gray_scale: s = np.mean(s, axis=2) s = np.reshape(s, [s.shape[0], s.shape[1], 1]) return s @staticmethod def from_agent_proto( worker_id: int, agent_info_list: List[AgentInfoProto], brain_params: BrainParameters, ) -> "BrainInfo": """ Converts list of agent infos to BrainInfo. """ vis_obs = BrainInfo._process_visual_observations(brain_params, agent_info_list) total_num_actions = sum(brain_params.vector_action_space_size) mask_actions = np.ones((len(agent_info_list), total_num_actions)) for agent_index, agent_info in enumerate(agent_info_list): if agent_info.action_mask is not None: if len(agent_info.action_mask) == total_num_actions: mask_actions[agent_index, :] = [ 0 if agent_info.action_mask[k] else 1 for k in range(total_num_actions) ] if any(np.isnan(x.reward) for x in agent_info_list): logger.warning( "An agent had a NaN reward for brain " + brain_params.brain_name ) vector_obs = BrainInfo._process_vector_observations( brain_params, agent_info_list ) agents = [f"${worker_id}-{x.id}" for x in agent_info_list] brain_info = BrainInfo( visual_observation=vis_obs, vector_observation=vector_obs, reward=[x.reward if not np.isnan(x.reward) else 0 for x in agent_info_list], agents=agents, local_done=[x.done for x in agent_info_list], vector_action=np.array([x.stored_vector_actions for x in agent_info_list]), max_reached=[x.max_step_reached for x in agent_info_list], action_mask=mask_actions, ) return brain_info @staticmethod def _process_visual_observations( brain_params: BrainParameters, agent_info_list: List[AgentInfoProto] ) -> List[np.ndarray]: visual_observation_protos: List[List[ObservationProto]] = [] # Grab the visual observations - need this together so we can iterate with the camera observations for agent in agent_info_list: agent_vis: List[ObservationProto] = [] for proto_obs in agent.observations: is_visual = len(proto_obs.shape) == 3 if is_visual: agent_vis.append(proto_obs) visual_observation_protos.append(agent_vis) vis_obs: List[np.ndarray] = [] for i in range(brain_params.number_visual_observations): # TODO check compression type, handle uncompressed visuals obs = [ BrainInfo.process_pixels( agent_obs[i].compressed_data, brain_params.camera_resolutions[i].gray_scale, ) for agent_obs in visual_observation_protos ] vis_obs += [obs] return vis_obs @staticmethod def _process_vector_observations( brain_params: BrainParameters, agent_info_list: List[AgentInfoProto] ) -> np.ndarray: if len(agent_info_list) == 0: vector_obs = np.zeros((0, brain_params.vector_observation_space_size)) else: stacked_obs = [] has_nan = False has_inf = False for agent_info in agent_info_list: vec_obs = [ obs for obs in agent_info.observations if len(obs.shape) == 1 ] # Concatenate vector obs proto_vector_obs: List[float] = [] for vo in vec_obs: # TODO consider itertools.chain here proto_vector_obs.extend(vo.float_data.data) np_obs = np.array(proto_vector_obs) # Check for NaNs or infs in the observations # If there's a NaN in the observations, the dot() result will be NaN # If there's an Inf (either sign) then the result will be Inf # See https://stackoverflow.com/questions/6736590/fast-check-for-nan-in-numpy for background # Note that a very large values (larger than sqrt(float_max)) will result in an Inf value here # This is OK though, worst case it results in an unnecessary (but harmless) nan_to_num call. d = np.dot(np_obs, np_obs) has_nan = has_nan or np.isnan(d) has_inf = has_inf or not np.isfinite(d) stacked_obs.append(np_obs) vector_obs = np.array(stacked_obs) # In we have any NaN or Infs, use np.nan_to_num to replace these with finite values if has_nan or has_inf: vector_obs = np.nan_to_num(vector_obs) if has_nan: logger.warning( f"An agent had a NaN observation for brain {brain_params.brain_name}" ) return vector_obs def safe_concat_lists(l1: Optional[List], l2: Optional[List]) -> Optional[List]: if l1 is None: if l2 is None: return None else: return l2.copy() else: if l2 is None: return l1.copy() else: copy = l1.copy() copy.extend(l2) return copy def safe_concat_np_ndarray( a1: Optional[np.ndarray], a2: Optional[np.ndarray] ) -> Optional[np.ndarray]: if a1 is not None and a1.size != 0: if a2 is not None and a2.size != 0: return np.append(a1, a2, axis=0) else: return a1.copy() elif a2 is not None and a2.size != 0: return a2.copy() return None # Renaming of dictionary of brain name to BrainInfo for clarity AllBrainInfo = Dict[str, BrainInfo]