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"""A simple multi-agent env with two agents playing rock paper scissors.
This demonstrates running the following policies in competition:
(1) heuristic policy of repeating the same move
(2) heuristic policy of beating the last opponent move
(3) LSTM/feedforward PG policies
(4) LSTM policy with custom entropy loss
"""
import argparse
import os
from pettingzoo.classic import rps_v2
import random
import ray
from ray import tune
from ray.rllib.agents.pg import PGTrainer, PGTFPolicy, PGTorchPolicy
from ray.rllib.agents.registry import get_trainer_class
from ray.rllib.env import PettingZooEnv
from ray.rllib.examples.policy.rock_paper_scissors_dummies import \
BeatLastHeuristic, AlwaysSameHeuristic
from ray.rllib.policy.policy import PolicySpec
from ray.rllib.utils.framework import try_import_tf, try_import_torch
from ray.rllib.utils.test_utils import check_learning_achieved
from ray.tune.registry import register_env
tf1, tf, tfv = try_import_tf()
torch, _ = try_import_torch()
parser = argparse.ArgumentParser()
parser.add_argument(
"--framework",
choices=["tf", "tf2", "tfe", "torch"],
default="tf",
help="The DL framework specifier.")
parser.add_argument(
"--as-test",
action="store_true",
help="Whether this script should be run as a test: --stop-reward must "
"be achieved within --stop-timesteps AND --stop-iters.")
parser.add_argument(
"--stop-iters",
type=int,
default=150,
help="Number of iterations to train.")
parser.add_argument(
"--stop-timesteps",
type=int,
default=100000,
help="Number of timesteps to train.")
parser.add_argument(
"--stop-reward",
type=float,
default=1000.0,
help="Reward at which we stop training.")
def env_creator(args):
env = rps_v2.env()
return env
register_env("RockPaperScissors",
lambda config: PettingZooEnv(env_creator(config)))
def run_same_policy(args, stop):
"""Use the same policy for both agents (trivial case)."""
config = {
"env": "RockPaperScissors",
"framework": args.framework,
}
results = tune.run("PG", config=config, stop=stop, verbose=1)
if args.as_test:
# Check vs 0.0 as we are playing a zero-sum game.
check_learning_achieved(results, 0.0)
def run_heuristic_vs_learned(args, use_lstm=False, trainer="PG"):
"""Run heuristic policies vs a learned agent.
The learned agent should eventually reach a reward of ~5 with
use_lstm=False, and ~7 with use_lstm=True. The reason the LSTM policy
can perform better is since it can distinguish between the always_same vs
beat_last heuristics.
"""
def select_policy(agent_id, episode, **kwargs):
if agent_id == "player_0":
return "learned"
else:
return random.choice(["always_same", "beat_last"])
config = {
"env": "RockPaperScissors",
"gamma": 0.9,
# Use GPUs iff `RLLIB_NUM_GPUS` env var set to > 0.
"num_gpus": int(os.environ.get("RLLIB_NUM_GPUS", "0")),
"num_workers": 0,
"num_envs_per_worker": 4,
"rollout_fragment_length": 10,
"train_batch_size": 200,
"metrics_num_episodes_for_smoothing": 200,
"multiagent": {
"policies_to_train": ["learned"],
"policies": {
"always_same": PolicySpec(policy_class=AlwaysSameHeuristic),
"beat_last": PolicySpec(policy_class=BeatLastHeuristic),
"learned": PolicySpec(config={
"model": {
"use_lstm": use_lstm
},
"framework": args.framework,
}),
},
"policy_mapping_fn": select_policy,
},
"framework": args.framework,
}
cls = get_trainer_class(trainer) if isinstance(trainer, str) else trainer
trainer_obj = cls(config=config)
for _ in range(args.stop_iters):
results = trainer_obj.train()
# Timesteps reached.
if "policy_always_same_reward" not in results["hist_stats"]:
reward_diff = 0
continue
reward_diff = sum(results["hist_stats"]["policy_learned_reward"])
if results["timesteps_total"] > args.stop_timesteps:
break
# Reward (difference) reached -> all good, return.
elif reward_diff > args.stop_reward:
return
# Reward (difference) not reached: Error if `as_test`.
if args.as_test:
raise ValueError(
"Desired reward difference ({}) not reached! Only got to {}.".
format(args.stop_reward, reward_diff))
def run_with_custom_entropy_loss(args, stop):
"""Example of customizing the loss function of an existing policy.
This performs about the same as the default loss does."""
def entropy_policy_gradient_loss(policy, model, dist_class, train_batch):
logits, _ = model(train_batch)
action_dist = dist_class(logits, model)
if args.framework == "torch":
# Required by PGTorchPolicy's stats fn.
model.tower_stats["policy_loss"] = torch.tensor([0.0])
policy.policy_loss = torch.mean(-0.1 * action_dist.entropy() - (
action_dist.logp(train_batch["actions"]) *
train_batch["advantages"]))
else:
policy.policy_loss = -0.1 * action_dist.entropy() - tf.reduce_mean(
action_dist.logp(train_batch["actions"]) *
train_batch["advantages"])
return policy.policy_loss
policy_cls = PGTorchPolicy if args.framework == "torch" \
else PGTFPolicy
EntropyPolicy = policy_cls.with_updates(
loss_fn=entropy_policy_gradient_loss)
class EntropyLossPG(PGTrainer):
def get_default_policy_class(self, config):
return EntropyPolicy
run_heuristic_vs_learned(args, use_lstm=True, trainer=EntropyLossPG)
if __name__ == "__main__":
args = parser.parse_args()
ray.init()
stop = {
"training_iteration": args.stop_iters,
"timesteps_total": args.stop_timesteps,
"episode_reward_mean": args.stop_reward,
}
run_same_policy(args, stop=stop)
print("run_same_policy: ok.")
run_heuristic_vs_learned(args, use_lstm=False)
print("run_heuristic_vs_learned(w/o lstm): ok.")
run_heuristic_vs_learned(args, use_lstm=True)
print("run_heuristic_vs_learned (w/ lstm): ok.")
run_with_custom_entropy_loss(args, stop=stop)
print("run_with_custom_entropy_loss: ok.")