OpenDILabCommunity/TicTacToe-play-with-bot-AlphaZero

Play TicTacToe-play-with-bot with AlphaZero Policy

Model Description

This implementation applies AlphaZero to the OpenAI/Gym/Atari TicTacToe-play-with-bot environment using LightZero and DI-engine.

LightZero is an efficient, easy-to-understand open-source toolkit that merges Monte Carlo Tree Search (MCTS) with Deep Reinforcement Learning (RL), simplifying their integration for developers and researchers. More details are in paper LightZero: A Unified Benchmark for Monte Carlo Tree Search in General Sequential Decision Scenarios.

Model Usage

Install the Dependencies

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# install huggingface_ding
git clone https://github.com/opendilab/huggingface_ding.git
pip3 install -e ./huggingface_ding/
# install environment dependencies if needed

pip3 install DI-engine[common_env,video]
pip3 install LightZero

Git Clone from Huggingface and Run the Model

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# running with trained model
python3 -u run.py

run.py

from lzero.agent import AlphaZeroAgent
from ding.config import Config
from easydict import EasyDict
import torch

# Pull model from files which are git cloned from huggingface
policy_state_dict = torch.load("pytorch_model.bin", map_location=torch.device("cpu"))
cfg = EasyDict(Config.file_to_dict("policy_config.py").cfg_dict)
# Instantiate the agent
agent = AlphaZeroAgent(
    env_id="TicTacToe-play-with-bot", exp_name="TicTacToe-play-with-bot-AlphaZero", cfg=cfg.exp_config, policy_state_dict=policy_state_dict
)
# Continue training
agent.train(step=5000)
# Render the new agent performance
agent.deploy(enable_save_replay=True)

Run Model by Using Huggingface_ding

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# running with trained model
python3 -u run.py

run.py

from lzero.agent import AlphaZeroAgent
from huggingface_ding import pull_model_from_hub

# Pull model from Hugggingface hub
policy_state_dict, cfg = pull_model_from_hub(repo_id="OpenDILabCommunity/TicTacToe-play-with-bot-AlphaZero")
# Instantiate the agent
agent = AlphaZeroAgent(
    env_id="TicTacToe-play-with-bot", exp_name="TicTacToe-play-with-bot-AlphaZero", cfg=cfg.exp_config, policy_state_dict=policy_state_dict
)
# Continue training
agent.train(step=5000)
# Render the new agent performance
agent.deploy(enable_save_replay=True)

Model Training

Train the Model and Push to Huggingface_hub

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#Training Your Own Agent
python3 -u train.py

train.py

from lzero.agent import AlphaZeroAgent
from huggingface_ding import push_model_to_hub

# Instantiate the agent
agent = AlphaZeroAgent(env_id="TicTacToe-play-with-bot", exp_name="TicTacToe-play-with-bot-AlphaZero")
# Train the agent
return_ = agent.train(step=int(500000))
# Push model to huggingface hub
push_model_to_hub(
    agent=agent.best,
    env_name="OpenAI/Gym/Atari",
    task_name="TicTacToe-play-with-bot",
    algo_name="AlphaZero",
    github_repo_url="https://github.com/opendilab/LightZero",
    github_doc_model_url=None,
    github_doc_env_url=None,
    installation_guide='''
pip3 install DI-engine[common_env,video]
pip3 install LightZero
''',
    usage_file_by_git_clone="./alphazero/tictactoe_play_with_bot_alphazero_deploy.py",
    usage_file_by_huggingface_ding="./alphazero/tictactoe_play_with_bot_alphazero_download.py",
    train_file="./alphazero/tictactoe_play_with_bot_alphazero.py",
    repo_id="OpenDILabCommunity/TicTacToe-play-with-bot-AlphaZero",
    platform_info="[LightZero](https://github.com/opendilab/LightZero) and [DI-engine](https://github.com/opendilab/di-engine)",
    model_description="**LightZero** is an efficient, easy-to-understand open-source toolkit that merges Monte Carlo Tree Search (MCTS) with Deep Reinforcement Learning (RL), simplifying their integration for developers and researchers. More details are in paper [LightZero: A Unified Benchmark for Monte Carlo Tree Search in General Sequential Decision Scenarios](https://huggingface.co/papers/2310.08348).",
    create_repo=True
)

Configuration

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exp_config = {
    'main_config': {
        'exp_name': 'TicTacToe-play-with-bot-AlphaZero',
        'seed': 0,
        'env': {
            'env_id': 'TicTacToe-play-with-bot',
            'board_size': 3,
            'battle_mode': 'play_with_bot_mode',
            'bot_action_type': 'v0',
            'channel_last': False,
            'collector_env_num': 8,
            'evaluator_env_num': 5,
            'n_evaluator_episode': 5,
            'manager': {
                'shared_memory': False
            },
            'agent_vs_human': False,
            'prob_random_agent': 0,
            'prob_expert_agent': 0,
            'scale': True,
            'alphazero_mcts_ctree': False,
            'save_replay_gif': False,
            'replay_path_gif': './replay_gif'
        },
        'policy': {
            'on_policy': False,
            'cuda': True,
            'multi_gpu': False,
            'bp_update_sync': True,
            'traj_len_inf': False,
            'model': {
                'observation_shape': [3, 3, 3],
                'action_space_size': 9,
                'num_res_blocks': 1,
                'num_channels': 16,
                'fc_value_layers': [8],
                'fc_policy_layers': [8]
            },
            'torch_compile': False,
            'tensor_float_32': False,
            'sampled_algo': False,
            'gumbel_algo': False,
            'update_per_collect': 50,
            'model_update_ratio': 0.1,
            'batch_size': 256,
            'optim_type': 'Adam',
            'learning_rate': 0.003,
            'weight_decay': 0.0001,
            'momentum': 0.9,
            'grad_clip_value': 0.5,
            'value_weight': 1.0,
            'collector_env_num': 8,
            'evaluator_env_num': 5,
            'lr_piecewise_constant_decay': False,
            'threshold_training_steps_for_final_lr': 500000,
            'manual_temperature_decay': False,
            'threshold_training_steps_for_final_temperature': 100000,
            'fixed_temperature_value': 0.25,
            'mcts': {
                'num_simulations': 25
            },
            'other': {
                'replay_buffer': {
                    'replay_buffer_size': 1000000,
                    'save_episode': False
                }
            },
            'cfg_type': 'AlphaZeroPolicyDict',
            'mcts_ctree': False,
            'simulation_env_name': 'tictactoe',
            'simulation_env_config_type': 'play_with_bot',
            'board_size': 3,
            'entropy_weight': 0.0,
            'n_episode': 8,
            'eval_freq': 2000
        },
        'wandb_logger': {
            'gradient_logger': False,
            'video_logger': False,
            'plot_logger': False,
            'action_logger': False,
            'return_logger': False
        }
    },
    'create_config': {
        'env': {
            'type': 'tictactoe',
            'import_names': ['zoo.board_games.tictactoe.envs.tictactoe_env']
        },
        'env_manager': {
            'type': 'subprocess'
        },
        'policy': {
            'type': 'alphazero',
            'import_names': ['lzero.policy.alphazero']
        },
        'collector': {
            'type': 'episode_alphazero',
            'import_names': ['lzero.worker.alphazero_collector']
        },
        'evaluator': {
            'type': 'alphazero',
            'import_names': ['lzero.worker.alphazero_evaluator']
        }
    }
}

Training Procedure

• Weights & Biases (wandb): monitor link

Model Information

• Github Repository: repo link
• Doc: Algorithm link
• Configuration: config link
• Demo: video
• Parameters total size: 51.13 KB
• Last Update Date: 2023-12-20

Environments

• Benchmark: OpenAI/Gym/Atari
• Task: TicTacToe-play-with-bot
• Gym version: 0.25.1
• DI-engine version: v0.5.0
• PyTorch version: 2.0.1+cu117
• Doc: Environments link