wehub-resource-sync
86 lines
3.3 KiB
Python
86 lines
3.3 KiB
Python
"""Example of running a multi-agent experiment w/ agents taking turns (sequence).
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This example:
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- demonstrates how to write your own (multi-agent) environment using RLlib's
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MultiAgentEnv API.
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- shows how to implement the `reset()` and `step()` methods of the env such that
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the agents act in a fixed sequence (taking turns).
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- shows how to configure and setup this environment class within an RLlib
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Algorithm config.
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- runs the experiment with the configured algo, trying to solve the environment.
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How to run this script
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----------------------
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`python [script file name].py`
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For debugging, use the following additional command line options
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`--no-tune --num-env-runners=0`
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which should allow you to set breakpoints anywhere in the RLlib code and
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have the execution stop there for inspection and debugging.
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For logging to your WandB account, use:
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`--wandb-key=[your WandB API key] --wandb-project=[some project name]
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--wandb-run-name=[optional: WandB run name (within the defined project)]`
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Results to expect
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-----------------
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You should see results similar to the following in your console output:
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+---------------------------+----------+--------+------------------+--------+
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| Trial name | status | iter | total time (s) | ts |
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|---------------------------+----------+--------+------------------+--------+
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| PPO_TicTacToe_957aa_00000 | RUNNING | 25 | 96.7452 | 100000 |
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+---------------------------+----------+--------+------------------+--------+
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+-------------------+------------------+------------------+
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| combined return | return player2 | return player1 |
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|-------------------+------------------+------------------|
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| -2 | 1.15 | -0.85 |
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+-------------------+------------------+------------------+
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Note that even though we are playing a zero-sum game, the overall return should start
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at some negative values due to the misplacement penalty of our (simplified) TicTacToe
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game.
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"""
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from ray.rllib.examples.envs.classes.multi_agent.tic_tac_toe import TicTacToe
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from ray.rllib.examples.utils import (
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add_rllib_example_script_args,
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run_rllib_example_script_experiment,
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)
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from ray.tune.registry import get_trainable_cls, register_env # noqa
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parser = add_rllib_example_script_args(
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default_reward=-4.0, default_iters=50, default_timesteps=100000
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)
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parser.set_defaults(
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num_agents=2,
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)
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if __name__ == "__main__":
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args = parser.parse_args()
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assert args.num_agents == 2, "Must set --num-agents=2 when running this script!"
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# You can also register the env creator function explicitly with:
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# register_env("tic_tac_toe", lambda cfg: TicTacToe())
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# Or allow the RLlib user to set more c'tor options via their algo config:
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# config.environment(env_config={[c'tor arg name]: [value]})
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# register_env("tic_tac_toe", lambda cfg: TicTacToe(cfg))
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base_config = (
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get_trainable_cls(args.algo)
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.get_default_config()
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.environment(TicTacToe)
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.multi_agent(
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# Define two policies.
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policies={"player1", "player2"},
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# Map agent "player1" to policy "player1" and agent "player2" to policy
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# "player2".
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policy_mapping_fn=lambda agent_id, episode, **kw: agent_id,
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)
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)
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run_rllib_example_script_experiment(base_config, args)
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