354 lines
15 KiB
Python
354 lines
15 KiB
Python
import logging
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import numpy as np
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from collections import defaultdict
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from metadrive.component.vehicle.vehicle_type import DefaultVehicle
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from metadrive.type import MetaDriveType
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from Env.scenario_env import MultiAgentScenarioEnv, PolicyVehicle
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from Env.inverse_dynamics import InverseDynamics
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class ExpertReplayEnv(MultiAgentScenarioEnv):
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def __init__(self, config=None):
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# Allow passing config without agent2policy since we don't use policies for replay
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if config is None:
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config = {}
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# Ensure we don't simulate physics for the controlled agents in the traditional sense
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# but we still need the engine to run
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super().__init__(config, agent2policy={})
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self.inverse_dynamics = InverseDynamics()
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self.expert_tracks = {}
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# Replay SDC/ego ("default_agent" in MetaDrive) as well; otherwise it will keep default action=0 and look stuck.
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self.replay_sdc = self.config.get("replay_sdc", True)
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self.sdc_track = None
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self.sdc_vehicle = None
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self.sdc_agent_id = "default_agent"
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def reset(self, seed=None):
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self.round = 0
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if self.logger is None:
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from metadrive.engine.logger import get_logger, set_log_level
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self.logger = get_logger()
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log_level = self.config.get("log_level", logging.INFO)
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set_log_level(log_level)
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self.lazy_init()
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self._reset_global_seed(seed)
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if self.engine is None:
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raise ValueError("Broken MetaDrive instance.")
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self.background_vehicles = {}
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self.expert_tracks = {}
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self.sdc_track = None
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self.sdc_vehicle = None
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# 在加载新场景前，必须清除上一轮通过 spawn_object 生成的物体，否则 engine.reset() 内 _object_clean_check 会报错
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# 从 engine 当前对象中按名称筛选（与 manager 无关的对象需在此清理），并强制销毁
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ids_to_clear = []
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for oid, obj in self.engine.get_objects().items():
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name = getattr(obj, "name", None) or getattr(obj, "id", None)
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if name and (str(name).startswith("controlled_") or str(name).startswith("bg_")):
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ids_to_clear.append(oid)
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if ids_to_clear:
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self.engine.clear_objects(ids_to_clear, force_destroy=True)
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self.controlled_agents.clear()
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self.controlled_agent_ids.clear()
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for aid in list(self.engine.agent_manager.active_agents.keys()):
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if aid.startswith("bg_") or aid.startswith("controlled_"):
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self.engine.agent_manager.active_agents.pop(aid, None)
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if self.replay_sdc and hasattr(self.engine, "traffic_manager"):
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sdc_sid = self.engine.traffic_manager.sdc_scenario_id
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self.sdc_track = self.engine.traffic_manager.current_traffic_data.get(sdc_sid, None)
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from Env.utils import filter_traffic_tracks_to_birth_lists
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traffic_data = self.engine.traffic_manager.current_traffic_data
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car_birth_info_list, self.background_vehicles, obj_to_clean = filter_traffic_tracks_to_birth_lists(
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traffic_data,
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self.engine.traffic_manager.sdc_scenario_id,
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self.engine.map_manager,
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)
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for entry in car_birth_info_list:
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sid = entry["scenario_id"]
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if sid in traffic_data:
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self.expert_tracks[sid] = traffic_data[sid]
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self.car_birth_info_list = car_birth_info_list
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for scenario_id in obj_to_clean:
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self.engine.traffic_manager.current_traffic_data.pop(scenario_id)
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self.engine.reset()
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self.reset_sensors()
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self.engine.taskMgr.step()
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self.lanes = self.engine.map_manager.current_map.road_network.graph
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if self.top_down_renderer is not None:
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self.top_down_renderer.clear()
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self.engine.top_down_renderer = None
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self.dones = {}
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self.episode_rewards = defaultdict(float)
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self.episode_lengths = defaultdict(int)
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self.controlled_agents.clear()
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self.controlled_agent_ids.clear()
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# We skip calling super().reset() to avoid double reset
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# But we need to ensure ScenarioEnv-specific setup is done if any.
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# ScenarioEnv.reset() basically does engine.reset() and some cleanup.
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# We covered most of it.
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self._spawn_controlled_agents()
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self._spawn_all_background_vehicles_at_init()
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# Ensure SDC/ego is moved to the correct initial expert state.
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if self.replay_sdc:
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self.sdc_vehicle = self.engine.agent_manager.active_agents.get(self.sdc_agent_id, None)
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if self.sdc_vehicle is not None and self.sdc_track is not None:
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valid = self.sdc_track["state"]["valid"]
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t0 = int(np.argmax(valid)) if valid.any() else 0
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pos0 = self.sdc_track["state"]["position"][t0]
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heading0 = self.sdc_track["state"]["heading"][t0]
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vel0 = self.sdc_track["state"]["velocity"][t0]
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self.sdc_vehicle.set_position(pos0)
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self.sdc_vehicle.set_heading_theta(heading0)
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self.sdc_vehicle.set_velocity(vel0)
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return self._get_all_obs()
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def _spawn_all_background_vehicles_at_init(self):
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"""Spawn all static background vehicles once at reset (no show_time filter; no removal by valid)."""
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for sid, car in self.background_vehicles.items():
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bg_id = f"bg_{car['id']}"
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if bg_id in self.engine.agent_manager.active_agents:
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continue
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vehicle_config = {}
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if 'length' in car and 'width' in car:
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vehicle_config = {
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"length": car['length'],
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"width": car['width']
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}
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v = self.engine.spawn_object(
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DefaultVehicle,
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name=bg_id,
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vehicle_config=vehicle_config,
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position=car['begin'],
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heading=car['heading']
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)
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v.set_velocity([0, 0])
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self.engine.agent_manager.active_agents[bg_id] = v
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v.valid_mask = car.get('valid')
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v.start_t = car['show_time']
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def _update_background_vehicles(self):
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# Static vehicles are spawned once at init and never removed (no spawn/remove by show_time or valid).
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pass
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def _spawn_controlled_agents(self):
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for car in self.car_birth_info_list:
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if car['show_time'] == self.round:
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agent_id = f"controlled_{car['id']}"
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# Check if we already have this agent (shouldn't happen with unique IDs but safety check)
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if agent_id in self.controlled_agents:
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continue
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# Handling ID flickering / merging
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# If this ID is new, check if there's an existing agent very close to its start position
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# that just disappeared? (Not implemented here, complex logic)
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# But we can check if there's an overlap with existing agents?
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# For now, just spawn.
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# Read vehicle type/size if available
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vehicle_config = {}
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if 'length' in car and 'width' in car:
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vehicle_config = {
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"length": car['length'],
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"width": car['width']
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}
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vehicle = self.engine.spawn_object(
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PolicyVehicle,
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name=agent_id,
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vehicle_config=vehicle_config,
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position=car['begin'],
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heading=car['heading']
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)
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vehicle.reset(position=car['begin'], heading=car['heading'])
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# We don't set policy or destination in the same way, or maybe we do for compatibility
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vehicle.set_destination(car['end'])
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# Store extra info for replay
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vehicle.expert_track = self.expert_tracks[car['scenario_id']]
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vehicle.original_id = car['id']
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self.controlled_agents[agent_id] = vehicle
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self.controlled_agent_ids.append(agent_id)
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self.engine.agent_manager.active_agents[agent_id] = vehicle
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def step(self, action_dict=None):
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# We ignore input action_dict for the purpose of controlling agents
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# Instead, we calculate what the action *should* be
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self.round += 1
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expert_actions = {}
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# 1. Update state of all controlled agents to the current timestep (self.round)
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# and compute action from (self.round-1) to (self.round).
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# Wait, usually step() moves T -> T+1.
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# Current state is T. We want to move to T+1.
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# So we need state at T and T+1.
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# Identify agents that are done (valid=0 at T+1 or T+1 >= length)
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agents_to_remove = []
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# Update SDC/ego first (otherwise it will stay still with default action=0)
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if self.replay_sdc and self.sdc_vehicle is not None and self.sdc_track is not None:
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next_step = self.round
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curr_step = self.round - 1
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if next_step < len(self.sdc_track["state"]["position"]) and self.sdc_track["state"]["valid"][next_step]:
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curr_state = {
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"position": self.sdc_track["state"]["position"][curr_step],
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"heading": self.sdc_track["state"]["heading"][curr_step],
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"velocity": self.sdc_track["state"]["velocity"][curr_step],
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}
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next_state = {
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"position": self.sdc_track["state"]["position"][next_step],
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"heading": self.sdc_track["state"]["heading"][next_step],
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"velocity": self.sdc_track["state"]["velocity"][next_step],
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}
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action, _ = self.inverse_dynamics.compute_action(curr_state, next_state, dt=0.1)
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expert_actions[self.sdc_agent_id] = action
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self.sdc_vehicle.set_position(next_state["position"])
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self.sdc_vehicle.set_heading_theta(next_state["heading"])
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self.sdc_vehicle.set_velocity(next_state["velocity"])
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self.sdc_vehicle.last_expert_action = action
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for agent_id, vehicle in self.controlled_agents.items():
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track = vehicle.expert_track
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# current_step = self.round - 1 # Since we incremented at start
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# But vehicle is currently at state corresponding to self.round - 1.
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# We want to move it to self.round.
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# Check bounds
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next_step = self.round
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curr_step = self.round - 1
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if next_step >= len(track['state']['position']):
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agents_to_remove.append(agent_id)
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continue
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valid = track['state']['valid'][next_step]
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if not valid:
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agents_to_remove.append(agent_id)
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continue
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# Get states
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curr_pos = track['state']['position'][curr_step]
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next_pos = track['state']['position'][next_step]
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curr_heading = track['state']['heading'][curr_step]
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next_heading = track['state']['heading'][next_step]
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curr_vel = track['state']['velocity'][curr_step]
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next_vel = track['state']['velocity'][next_step]
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# Prepare state dicts for Inverse Dynamics
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curr_state = {
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'position': curr_pos,
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'heading': curr_heading,
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'velocity': curr_vel
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}
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next_state = {
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'position': next_pos,
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'heading': next_heading,
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'velocity': next_vel
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}
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# Calculate action
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action, raw_info = self.inverse_dynamics.compute_action(curr_state, next_state, dt=0.1) # Waymo is 10Hz?
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expert_actions[agent_id] = action
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# Force update vehicle state
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vehicle.set_position(next_pos)
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vehicle.set_heading_theta(next_heading)
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vehicle.set_velocity(next_vel)
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# Also record this action in the vehicle for later retrieval if needed
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vehicle.last_expert_action = action
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# Remove finished agents
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for agent_id in agents_to_remove:
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vehicle = self.controlled_agents[agent_id]
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self.controlled_agents.pop(agent_id)
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self.controlled_agent_ids.remove(agent_id)
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self.engine.agent_manager.active_agents.pop(agent_id, None)
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self.engine.clear_objects([vehicle.id])
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# Step physics world to update sensors/collision detection
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# We don't need full integration, but we need to update the physics world state
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self.engine.taskMgr.step()
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# Spawn new agents for this turn
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self._spawn_controlled_agents()
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self._update_background_vehicles()
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# Get observations
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obs = self._get_all_obs()
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rewards = {aid: 0.0 for aid in self.controlled_agents}
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dones = {aid: False for aid in self.controlled_agents}
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dones["__all__"] = (self.round >= self.config["horizon"]) or (len(self.controlled_agents) == 0 and self.round > 190) # Waymo scenarios are usually ~198 steps (20s @ 10Hz) or 90 steps (9s)
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infos = {aid: {"expert_action": expert_actions.get(aid, np.zeros(2))} for aid in self.controlled_agents}
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return obs, rewards, dones, infos
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def _get_all_obs(self):
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# Implement custom observation: 30m range, 10 nearest vehicles
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obs_dict = {}
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for agent_id, vehicle in self.controlled_agents.items():
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# 1. Ego State
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ego_state = [
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vehicle.position[0], vehicle.position[1],
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vehicle.velocity[0], vehicle.velocity[1],
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vehicle.heading_theta
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]
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# 2. Neighbors
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neighbors = []
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# Iterate through all vehicles in the engine
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candidates = []
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for other_id, other_vehicle in self.engine.agent_manager.active_agents.items():
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if other_id == agent_id:
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continue
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dist = np.linalg.norm(vehicle.position - other_vehicle.position)
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if dist < 30.0:
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candidates.append((dist, other_vehicle))
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# Sort by distance
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candidates.sort(key=lambda x: x[0])
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# Take top 10
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top_10 = candidates[:10]
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neighbor_feats = []
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for _, neighbor in top_10:
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neighbor_feats.extend([
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neighbor.position[0] - vehicle.position[0], # Relative pos
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neighbor.position[1] - vehicle.position[1],
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neighbor.velocity[0], # Absolute vel? or Relative? Usually relative in MultiAgent
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neighbor.velocity[1]
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])
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# Pad if < 10
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missing = 10 - len(top_10)
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if missing > 0:
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neighbor_feats.extend([0.0] * (4 * missing))
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# Flatten
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obs = np.array(ego_state + neighbor_feats, dtype=np.float32)
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obs_dict[agent_id] = obs
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return obs_dict
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