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https://github.com/vale981/ray
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fcf897ee72
update rllib example to use Tuner API. Signed-off-by: xwjiang2010 <xwjiang2010@gmail.com>
125 lines
3.9 KiB
Python
125 lines
3.9 KiB
Python
"""Example of hierarchical training using the multi-agent API.
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The example env is that of a "windy maze". The agent observes the current wind
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direction and can either choose to stand still, or move in that direction.
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You can try out the env directly with:
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$ python hierarchical_training.py --flat
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A simple hierarchical formulation involves a high-level agent that issues goals
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(i.e., go north / south / east / west), and a low-level agent that executes
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these goals over a number of time-steps. This can be implemented as a
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multi-agent environment with a top-level agent and low-level agents spawned
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for each higher-level action. The lower level agent is rewarded for moving
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in the right direction.
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You can try this formulation with:
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$ python hierarchical_training.py # gets ~100 rew after ~100k timesteps
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Note that the hierarchical formulation actually converges slightly slower than
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using --flat in this example.
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"""
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import argparse
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from gym.spaces import Discrete, Tuple
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import logging
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import os
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import ray
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from ray import air, tune
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from ray.rllib.examples.env.windy_maze_env import WindyMazeEnv, HierarchicalWindyMazeEnv
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from ray.rllib.utils.test_utils import check_learning_achieved
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parser = argparse.ArgumentParser()
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parser.add_argument("--flat", action="store_true")
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parser.add_argument(
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"--framework",
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choices=["tf", "tf2", "tfe", "torch"],
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default="tf",
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help="The DL framework specifier.",
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)
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parser.add_argument(
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"--as-test",
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action="store_true",
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help="Whether this script should be run as a test: --stop-reward must "
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"be achieved within --stop-timesteps AND --stop-iters.",
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)
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parser.add_argument(
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"--stop-iters", type=int, default=200, help="Number of iterations to train."
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)
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parser.add_argument(
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"--stop-timesteps", type=int, default=100000, help="Number of timesteps to train."
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)
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parser.add_argument(
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"--stop-reward", type=float, default=0.0, help="Reward at which we stop training."
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)
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logger = logging.getLogger(__name__)
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if __name__ == "__main__":
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args = parser.parse_args()
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ray.init()
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stop = {
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"training_iteration": args.stop_iters,
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"timesteps_total": args.stop_timesteps,
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"episode_reward_mean": args.stop_reward,
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}
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if args.flat:
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results = tune.Tuner(
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"PPO",
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run_config=air.RunConfig(
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stop=stop,
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),
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param_space={
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"env": WindyMazeEnv,
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"num_workers": 0,
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"framework": args.framework,
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},
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).fit()
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else:
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maze = WindyMazeEnv(None)
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def policy_mapping_fn(agent_id, episode, worker, **kwargs):
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if agent_id.startswith("low_level_"):
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return "low_level_policy"
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else:
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return "high_level_policy"
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config = {
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"env": HierarchicalWindyMazeEnv,
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"num_workers": 0,
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"entropy_coeff": 0.01,
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"multiagent": {
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"policies": {
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"high_level_policy": (
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None,
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maze.observation_space,
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Discrete(4),
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{"gamma": 0.9},
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),
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"low_level_policy": (
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None,
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Tuple([maze.observation_space, Discrete(4)]),
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maze.action_space,
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{"gamma": 0.0},
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),
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},
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"policy_mapping_fn": policy_mapping_fn,
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},
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"framework": args.framework,
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# Use GPUs iff `RLLIB_NUM_GPUS` env var set to > 0.
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"num_gpus": int(os.environ.get("RLLIB_NUM_GPUS", "0")),
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}
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results = tune.Tuner(
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"PPO", run_config=air.RunConfig(stop=stop, verbose=1), param_space=config
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).fit()
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if args.as_test:
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check_learning_achieved(results, args.stop_reward)
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ray.shutdown()
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