parallel_multiagentArena.py

from unray_bridge.envs.envs import MultiAgentArena
from unray_bridge.envs.base_env import MultiAgentEnv

from unray_bridge.unray_config import UnrayConfig

from ray.rllib.algorithms.ppo import PPOConfig


if __name__ == '__main__':
    
    ppo_config = PPOConfig()

    ppo_config = ppo_config.training(gamma=0.9, lr=0.01, kl_coeff=0.3)  
    ppo_config = ppo_config.resources(num_gpus=0)  
    ppo_config = ppo_config.rollouts(num_rollout_workers=4)  
    
    
    arena_config = MultiAgentArena.env_config

    arena = MultiAgentEnv(arena_config, "multiagents-arena")
    
    unray_config = UnrayConfig()
    algo = unray_config.configure_algo(ppo_config, arena)

    for i in range(2):
        print(f"-------------------TRAINING ITERATION {algo.training_iteration}-------------------")
        result = algo.train()

        
    print(result['episode_reward_mean'])