ray/rllib/examples/nested_action_spaces.py
Sven Mika 57544b1ff9
[RLlib] Examples folder restructuring (Model examples; final part). (#8278)
- This PR completes any previously missing PyTorch Model counterparts to TFModels in examples/models.
- It also makes sure, all example scripts in the rllib/examples folder are tested for both frameworks and learn the given task (this is often currently not checked) using a --as-test flag in connection with a --stop-reward.
2020-05-12 08:23:10 +02:00

60 lines
2 KiB
Python

import argparse
from gym.spaces import Dict, Tuple, Box, Discrete
import ray
import ray.tune as tune
from ray.tune.registry import register_env
from ray.rllib.examples.env.nested_space_repeat_after_me_env import \
NestedSpaceRepeatAfterMeEnv
from ray.rllib.utils.test_utils import check_learning_achieved
parser = argparse.ArgumentParser()
parser.add_argument("--run", type=str, default="PPO")
parser.add_argument("--torch", action="store_true")
parser.add_argument("--as-test", action="store_true")
parser.add_argument("--stop-reward", type=float, default=0.0)
parser.add_argument("--stop-iters", type=int, default=100)
parser.add_argument("--stop-timesteps", type=int, default=100000)
parser.add_argument("--num-cpus", type=int, default=0)
if __name__ == "__main__":
args = parser.parse_args()
ray.init(num_cpus=args.num_cpus or None)
register_env("NestedSpaceRepeatAfterMeEnv",
lambda c: NestedSpaceRepeatAfterMeEnv(c))
config = {
"env": "NestedSpaceRepeatAfterMeEnv",
"env_config": {
"space": Dict({
"a": Tuple(
[Dict({
"d": Box(-10.0, 10.0, ()),
"e": Discrete(2)
})]),
"b": Box(-10.0, 10.0, (2, )),
"c": Discrete(4)
}),
},
"entropy_coeff": 0.00005, # We don't want high entropy in this Env.
"gamma": 0.0, # No history in Env (bandit problem).
"lr": 0.0005,
"num_envs_per_worker": 20,
"num_sgd_iter": 4,
"num_workers": 0,
"vf_loss_coeff": 0.01,
"use_pytorch": args.torch,
}
stop = {
"training_iteration": args.stop_iters,
"episode_reward_mean": args.stop_reward,
"timesteps_total": args.stop_timesteps,
}
results = tune.run(args.run, config=config, stop=stop)
if args.as_test:
check_learning_achieved(results, args.stop_reward)
ray.shutdown()