db50bca7fd
* scenarionet training * wandb * train utils * fix callback * run PPO * use pg test * save path * use torch * add dependency * update ignore * update training * large model * use curriculum training * add time to exp name * storage_path * restore * update training * use my key * add log message * check seed * restore callback * restore call bacl * add log message * add logging message * restore ray1.4 * length 500 * ray 100 * wandb * use tf * more levels * add callback * 10 worker * show level * no env horizon * callback result level * more call back * add diffuculty * add mroen stat * mroe stat * show levels * add callback * new * ep len 600 * fix setup * fix stepup * fix to 3.8 * update setup * parallel worker! * new exp * add callback * lateral dist * pg dataset * evaluate * modify config * align config * train single RL * update training script * 100w eval * less eval to reveal * 2000 env eval * new trianing * eval 1000 * update eval * more workers * more worker * 20 worker * dataset to database * split tool! * split dataset * try fix * train 003 * fix mapping * fix test * add waymo tqdm * utils * fix bug * fix bug * waymo * int type * 8 worker read * disable * read file * add log message * check existence * dist 0 * int * check num * suprass warning * add filter API * filter * store map false * new * ablation * filter * fix * update filyter * reanme to from * random select * add overlapping checj * fix * new training sceheme * new reward * add waymo train script * waymo different config * copy raw data * fix bug * add tqdm * update readme * waymo * pg * max lateral dist 3 * pg * crash_done instead of penalty * no crash done * gpu * update eval script * steering range penalty * evaluate * finish pg * update setup * fix bug * test * fix * add on line * train nuplan * generate sensor * udpate training * static obj * multi worker eval * filx bug * use ray for testing * eval! * filter senario * id filter * fox bug * dist = 2 * filter * eval * eval ret * ok * update training pg * test before use * store data=False * collect figures * capture pic --------- Co-authored-by: Quanyi Li <quanyi@bolei-gpu02.cs.ucla.edu>
111 lines
6.2 KiB
Python
111 lines
6.2 KiB
Python
from typing import Dict
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import numpy as np
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from ray.rllib.agents.callbacks import DefaultCallbacks
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from ray.rllib.env import BaseEnv
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from ray.rllib.evaluation import MultiAgentEpisode, RolloutWorker
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from ray.rllib.policy import Policy
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class DrivingCallbacks(DefaultCallbacks):
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def on_episode_start(
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self, *, worker: RolloutWorker, base_env: BaseEnv, policies: Dict[str, Policy], episode: MultiAgentEpisode,
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env_index: int, **kwargs
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):
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episode.user_data["velocity"] = []
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episode.user_data["steering"] = []
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episode.user_data["step_reward"] = []
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episode.user_data["acceleration"] = []
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episode.user_data["lateral_dist"] = []
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episode.user_data["cost"] = []
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episode.user_data["num_crash_vehicle"] = []
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episode.user_data["num_crash_human"] = []
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episode.user_data["num_crash_object"] = []
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episode.user_data["num_on_line"] = []
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episode.user_data["step_reward_lateral"] = []
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episode.user_data["step_reward_heading"] = []
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episode.user_data["step_reward_action_smooth"] = []
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def on_episode_step(
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self, *, worker: RolloutWorker, base_env: BaseEnv, episode: MultiAgentEpisode, env_index: int, **kwargs
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):
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info = episode.last_info_for()
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if info is not None:
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episode.user_data["velocity"].append(info["velocity"])
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episode.user_data["steering"].append(info["steering"])
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episode.user_data["step_reward"].append(info["step_reward"])
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episode.user_data["acceleration"].append(info["acceleration"])
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episode.user_data["lateral_dist"].append(info["lateral_dist"])
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episode.user_data["cost"].append(info["cost"])
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for x in ["num_crash_vehicle", "num_crash_object", "num_crash_human", "num_on_line"]:
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episode.user_data[x].append(info[x])
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for x in ["step_reward_lateral", "step_reward_heading", "step_reward_action_smooth"]:
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episode.user_data[x].append(info[x])
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def on_episode_end(
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self, worker: RolloutWorker, base_env: BaseEnv, policies: Dict[str, Policy], episode: MultiAgentEpisode,
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**kwargs
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):
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arrive_dest = episode.last_info_for()["arrive_dest"]
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crash = episode.last_info_for()["crash"]
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out_of_road = episode.last_info_for()["out_of_road"]
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max_step_rate = not (arrive_dest or crash or out_of_road)
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episode.custom_metrics["success_rate"] = float(arrive_dest)
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episode.custom_metrics["crash_rate"] = float(crash)
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episode.custom_metrics["out_of_road_rate"] = float(out_of_road)
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episode.custom_metrics["max_step_rate"] = float(max_step_rate)
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episode.custom_metrics["velocity_max"] = float(np.max(episode.user_data["velocity"]))
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episode.custom_metrics["velocity_mean"] = float(np.mean(episode.user_data["velocity"]))
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episode.custom_metrics["velocity_min"] = float(np.min(episode.user_data["velocity"]))
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episode.custom_metrics["lateral_dist_min"] = float(np.min(episode.user_data["lateral_dist"]))
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episode.custom_metrics["lateral_dist_max"] = float(np.max(episode.user_data["lateral_dist"]))
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episode.custom_metrics["lateral_dist_mean"] = float(np.mean(episode.user_data["lateral_dist"]))
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episode.custom_metrics["steering_max"] = float(np.max(episode.user_data["steering"]))
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episode.custom_metrics["steering_mean"] = float(np.mean(episode.user_data["steering"]))
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episode.custom_metrics["steering_min"] = float(np.min(episode.user_data["steering"]))
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episode.custom_metrics["acceleration_min"] = float(np.min(episode.user_data["acceleration"]))
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episode.custom_metrics["acceleration_mean"] = float(np.mean(episode.user_data["acceleration"]))
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episode.custom_metrics["acceleration_max"] = float(np.max(episode.user_data["acceleration"]))
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episode.custom_metrics["step_reward_max"] = float(np.max(episode.user_data["step_reward"]))
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episode.custom_metrics["step_reward_mean"] = float(np.mean(episode.user_data["step_reward"]))
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episode.custom_metrics["step_reward_min"] = float(np.min(episode.user_data["step_reward"]))
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episode.custom_metrics["cost"] = float(sum(episode.user_data["cost"]))
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for x in ["num_crash_vehicle", "num_crash_object", "num_crash_human", "num_on_line"]:
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episode.custom_metrics[x] = float(sum(episode.user_data[x]))
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for x in ["step_reward_lateral", "step_reward_heading", "step_reward_action_smooth"]:
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episode.custom_metrics[x] = float(np.mean(episode.user_data[x]))
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episode.custom_metrics["route_completion"] = float(episode.last_info_for()["route_completion"])
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episode.custom_metrics["curriculum_level"] = int(episode.last_info_for()["curriculum_level"])
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episode.custom_metrics["scenario_index"] = int(episode.last_info_for()["scenario_index"])
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episode.custom_metrics["track_length"] = float(episode.last_info_for()["track_length"])
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episode.custom_metrics["num_stored_maps"] = int(episode.last_info_for()["num_stored_maps"])
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episode.custom_metrics["scenario_difficulty"] = float(episode.last_info_for()["scenario_difficulty"])
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episode.custom_metrics["data_coverage"] = float(episode.last_info_for()["data_coverage"])
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episode.custom_metrics["curriculum_success"] = float(episode.last_info_for()["curriculum_success"])
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episode.custom_metrics["curriculum_route_completion"] = float(
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episode.last_info_for()["curriculum_route_completion"])
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def on_train_result(self, *, trainer, result: dict, **kwargs):
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result["success"] = np.nan
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result["out"] = np.nan
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result["max_step"] = np.nan
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result["level"] = np.nan
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result["length"] = result["episode_len_mean"]
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result["coverage"] = np.nan
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if "custom_metrics" not in result:
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return
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if "success_rate_mean" in result["custom_metrics"]:
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result["success"] = result["custom_metrics"]["success_rate_mean"]
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result["out"] = result["custom_metrics"]["out_of_road_rate_mean"]
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result["max_step"] = result["custom_metrics"]["max_step_rate_mean"]
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result["level"] = result["custom_metrics"]["curriculum_level_mean"]
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result["coverage"] = result["custom_metrics"]["data_coverage_mean"]
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