runtime/env_manager/rbs_gym/scripts/evaluate.py

294 lines
8.8 KiB
Python
Executable file

#!/usr/bin/env -S python3 -O
import argparse
import os
from typing import Dict
import numpy as np
import torch as th
import yaml
from stable_baselines3.common.utils import set_random_seed
from stable_baselines3.common.vec_env import DummyVecEnv, VecEnv, VecEnvWrapper
from rbs_gym import envs as gz_envs
from rbs_gym.utils import create_test_env, get_latest_run_id, get_saved_hyperparams
from rbs_gym.utils.utils import ALGOS, StoreDict, str2bool
def main(args: Dict):
if args.exp_id == 0:
args.exp_id = get_latest_run_id(
os.path.join(args.log_folder, args.algo), args.env
)
print(f"Loading latest experiment, id={args.exp_id}")
# Sanity checks
if args.exp_id > 0:
log_path = os.path.join(args.log_folder, args.algo, f"{args.env}_{args.exp_id}")
else:
log_path = os.path.join(args.log_folder, args.algo)
assert os.path.isdir(log_path), f"The {log_path} folder was not found"
found = False
for ext in ["zip"]:
model_path = os.path.join(log_path, f"{args.env}.{ext}")
found = os.path.isfile(model_path)
if found:
break
if args.load_best:
model_path = os.path.join(log_path, "best_model.zip")
found = os.path.isfile(model_path)
if args.load_checkpoint is not None:
model_path = os.path.join(
log_path, f"rl_model_{args.load_checkpoint}_steps.zip"
)
found = os.path.isfile(model_path)
if not found:
raise ValueError(
f"No model found for {args.algo} on {args.env}, path: {model_path}"
)
off_policy_algos = ["qrdqn", "dqn", "ddpg", "sac", "her", "td3", "tqc"]
if args.algo in off_policy_algos:
args.n_envs = 1
set_random_seed(args.seed)
if args.num_threads > 0:
if args.verbose > 1:
print(f"Setting torch.num_threads to {args.num_threads}")
th.set_num_threads(args.num_threads)
stats_path = os.path.join(log_path, args.env)
hyperparams, stats_path = get_saved_hyperparams(
stats_path, norm_reward=args.norm_reward, test_mode=True
)
# load env_kwargs if existing
env_kwargs = {}
args_path = os.path.join(log_path, args.env, "args.yml")
if os.path.isfile(args_path):
with open(args_path, "r") as f:
# pytype: disable=module-attr
loaded_args = yaml.load(f, Loader=yaml.UnsafeLoader)
if loaded_args["env_kwargs"] is not None:
env_kwargs = loaded_args["env_kwargs"]
# overwrite with command line arguments
if args.env_kwargs is not None:
env_kwargs.update(args.env_kwargs)
log_dir = args.reward_log if args.reward_log != "" else None
env = create_test_env(
args.env,
n_envs=args.n_envs,
stats_path=stats_path,
seed=args.seed,
log_dir=log_dir,
should_render=not args.no_render,
hyperparams=hyperparams,
env_kwargs=env_kwargs,
)
kwargs = dict(seed=args.seed)
if args.algo in off_policy_algos:
# Dummy buffer size as we don't need memory to evaluate the trained agent
kwargs.update(dict(buffer_size=1))
custom_objects = {'observation_space': env.observation_space, 'action_space': env.action_space}
model = ALGOS[args.algo].load(model_path, env=env, custom_objects=custom_objects, **kwargs)
obs = env.reset()
# Deterministic by default
stochastic = args.stochastic
deterministic = not stochastic
print(
f"Evaluating for {args.n_episodes} episodes with a",
"deterministic" if deterministic else "stochastic",
"policy.",
)
state = None
episode_reward = 0.0
episode_rewards, episode_lengths, success_episode_lengths = [], [], []
ep_len = 0
episode = 0
# For HER, monitor success rate
successes = []
while episode < args.n_episodes:
action, state = model.predict(obs, state=state, deterministic=deterministic)
obs, reward, done, infos = env.step(action)
if not args.no_render:
env.render("human")
episode_reward += reward[0]
ep_len += 1
if done and args.verbose > 0:
episode += 1
print(f"--- Episode {episode}/{args.n_episodes}")
# NOTE: for env using VecNormalize, the mean reward
# is a normalized reward when `--norm_reward` flag is passed
print(f"Episode Reward: {episode_reward:.2f}")
episode_rewards.append(episode_reward)
print("Episode Length", ep_len)
episode_lengths.append(ep_len)
if infos[0].get("is_success") is not None:
print("Success?:", infos[0].get("is_success", False))
successes.append(infos[0].get("is_success", False))
if infos[0].get("is_success"):
success_episode_lengths.append(ep_len)
print(f"Current success rate: {100 * np.mean(successes):.2f}%")
episode_reward = 0.0
ep_len = 0
state = None
if args.verbose > 0 and len(successes) > 0:
print(f"Success rate: {100 * np.mean(successes):.2f}%")
if args.verbose > 0 and len(episode_rewards) > 0:
print(
f"Mean reward: {np.mean(episode_rewards):.2f} "
f"+/- {np.std(episode_rewards):.2f}"
)
if args.verbose > 0 and len(episode_lengths) > 0:
print(
f"Mean episode length: {np.mean(episode_lengths):.2f} "
f"+/- {np.std(episode_lengths):.2f}"
)
if args.verbose > 0 and len(success_episode_lengths) > 0:
print(
f"Mean episode length of successful episodes: {np.mean(success_episode_lengths):.2f} "
f"+/- {np.std(success_episode_lengths):.2f}"
)
# Workaround for https://github.com/openai/gym/issues/893
if not args.no_render:
if args.n_envs == 1 and "Bullet" not in args.env and isinstance(env, VecEnv):
# DummyVecEnv
# Unwrap env
while isinstance(env, VecEnvWrapper):
env = env.venv
if isinstance(env, DummyVecEnv):
env.envs[0].env.close()
else:
env.close()
else:
# SubprocVecEnv
env.close()
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Environment and its parameters
parser.add_argument(
"--env", type=str, default="Reach-Gazebo-v0", help="Environment ID"
)
parser.add_argument(
"--env-kwargs",
type=str,
nargs="+",
action=StoreDict,
help="Optional keyword argument to pass to the env constructor",
)
parser.add_argument("--n-envs", type=int, default=1, help="Number of environments")
# Algorithm
parser.add_argument(
"--algo",
type=str,
choices=list(ALGOS.keys()),
required=False,
default="sac",
help="RL algorithm to use during the training",
)
parser.add_argument(
"--num-threads",
type=int,
default=-1,
help="Number of threads for PyTorch (-1 to use default)",
)
# Test duration
parser.add_argument(
"-n",
"--n-episodes",
type=int,
default=200,
help="Number of evaluation episodes",
)
# Random seed
parser.add_argument("--seed", type=int, default=0, help="Random generator seed")
# Model to test
parser.add_argument(
"-f", "--log-folder", type=str, default="logs", help="Path to the log directory"
)
parser.add_argument(
"--exp-id",
type=int,
default=0,
help="Experiment ID (default: 0: latest, -1: no exp folder)",
)
parser.add_argument(
"--load-best",
type=str2bool,
default=False,
help="Load best model instead of last model if available",
)
parser.add_argument(
"--load-checkpoint",
type=int,
help="Load checkpoint instead of last model if available, you must pass the number of timesteps corresponding to it",
)
# Deterministic/stochastic actions
parser.add_argument(
"--stochastic",
type=str2bool,
default=False,
help="Use stochastic actions instead of deterministic",
)
# Logging
parser.add_argument(
"--reward-log", type=str, default="reward_logs", help="Where to log reward"
)
parser.add_argument(
"--norm-reward",
type=str2bool,
default=False,
help="Normalize reward if applicable (trained with VecNormalize)",
)
# Disable render
parser.add_argument(
"--no-render",
type=str2bool,
default=False,
help="Do not render the environment (useful for tests)",
)
# Verbosity
parser.add_argument(
"--verbose", type=int, default=1, help="Verbose mode (0: no output, 1: INFO)"
)
args, unknown = parser.parse_known_args()
main(args)