PongNoFrameskip-v4-IMPALA

Play PongNoFrameskip-v4 with IMPALA Policy

Model Description

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This is a simple IMPALA implementation to OpenAI/Gym/Atari PongNoFrameskip-v4 using the DI-engine library and the DI-zoo.

DI-engine is a python library for solving general decision intelligence problems, which is based on implementations of reinforcement learning framework using PyTorch or JAX. This library aims to standardize the reinforcement learning framework across different algorithms, benchmarks, environments, and to support both academic researches and prototype applications. Besides, self-customized training pipelines and applications are supported by reusing different abstraction levels of DI-engine reinforcement learning framework.

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]

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Git Clone from Huggingface and Run the Model

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# running with trained model
python3 -u run.py

run.py

from ding.bonus import IMPALAAgent
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 = IMPALAAgent(
    env_id="PongNoFrameskip-v4", exp_name="PongNoFrameskip-v4-IMPALA", 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)

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Run Model by Using Huggingface_ding

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# running with trained model
python3 -u run.py

run.py

from ding.bonus import IMPALAAgent
from huggingface_ding import pull_model_from_hub

# Pull model from Hugggingface hub
policy_state_dict, cfg = pull_model_from_hub(repo_id="OpenDILabCommunity/PongNoFrameskip-v4-IMPALA")
# Instantiate the agent
agent = IMPALAAgent(
    env_id="PongNoFrameskip-v4", exp_name="PongNoFrameskip-v4-IMPALA", 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)

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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 ding.bonus import IMPALAAgent
from huggingface_ding import push_model_to_hub

# Instantiate the agent
agent = IMPALAAgent(env_id="PongNoFrameskip-v4", exp_name="PongNoFrameskip-v4-IMPALA")
# Train the agent
return_ = agent.train(step=int(20000000))
# Push model to huggingface hub
push_model_to_hub(
    agent=agent.best,
    env_name="OpenAI/Gym/Atari",
    task_name="PongNoFrameskip-v4",
    algo_name="IMPALA",
    wandb_url=return_.wandb_url,
    github_repo_url="https://github.com/opendilab/DI-engine",
    github_doc_model_url="https://di-engine-docs.readthedocs.io/en/latest/12_policies/impala.html",
    github_doc_env_url="https://di-engine-docs.readthedocs.io/en/latest/13_envs/atari.html",
    installation_guide="pip3 install DI-engine[common_env,video]",
    usage_file_by_git_clone="./impala/pong_impala_deploy.py",
    usage_file_by_huggingface_ding="./impala/pong_impala_download.py",
    train_file="./impala/pong_impala.py",
    repo_id="OpenDILabCommunity/PongNoFrameskip-v4-IMPALA",
    create_repo=True
)

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Configuration

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exp_config = {
    'env': {
        'manager': {
            'episode_num': float("inf"),
            'max_retry': 1,
            'retry_type': 'reset',
            'auto_reset': True,
            'step_timeout': None,
            'reset_timeout': None,
            'retry_waiting_time': 0.1,
            'cfg_type': 'BaseEnvManagerDict'
        },
        'stop_value': 30,
        'n_evaluator_episode': 8,
        'env_id': 'PongNoFrameskip-v4',
        'collector_env_num': 8,
        'evaluator_env_num': 8,
        'fram_stack': 4,
        'env_wrapper': 'atari_default'
    },
    'policy': {
        'model': {
            'obs_shape': [4, 84, 84],
            'action_shape': 6,
            'encoder_hidden_size_list': [64, 128, 256],
            'critic_head_hidden_size': 256,
            'critic_head_layer_num': 2,
            'actor_head_hidden_size': 256,
            'actor_head_layer_num': 2
        },
        'learn': {
            'learner': {
                'train_iterations': 1000000000,
                'dataloader': {
                    'num_workers': 0
                },
                'log_policy': True,
                'hook': {
                    'load_ckpt_before_run': '',
                    'log_show_after_iter': 100,
                    'save_ckpt_after_iter': 10000,
                    'save_ckpt_after_run': True
                },
                'cfg_type': 'BaseLearnerDict'
            },
            'update_per_collect': 2,
            'batch_size': 128,
            'learning_rate': 0.0006,
            'value_weight': 0.5,
            'entropy_weight': 0.01,
            'discount_factor': 0.99,
            'lambda_': 0.95,
            'rho_clip_ratio': 1.0,
            'c_clip_ratio': 1.0,
            'rho_pg_clip_ratio': 1.0,
            'grad_clip_type': 'clip_norm',
            'clip_value': 0.5
        },
        'collect': {
            'collector': {
                'collect_print_freq': 1000
            },
            'n_sample': 16,
            'unroll_len': 64
        },
        'eval': {
            'evaluator': {
                'eval_freq': 2000,
                'render': {
                    'render_freq': -1,
                    'mode': 'train_iter'
                },
                'figure_path': None,
                'cfg_type': 'InteractionSerialEvaluatorDict',
                'stop_value': 30,
                'n_episode': 8
            }
        },
        'other': {
            'replay_buffer': {
                'replay_buffer_size': 10000,
                'max_use': 16,
                'sliced': False
            }
        },
        'on_policy': False,
        'cuda': True,
        'multi_gpu': False,
        'bp_update_sync': True,
        'traj_len_inf': False,
        'type': 'impala',
        'priority': False,
        'priority_IS_weight': False,
        'action_space': 'discrete',
        'unroll_len': 64,
        'transition_with_policy_data': True,
        'cfg_type': 'IMPALAPolicyDict'
    },
    'exp_name': 'PongNoFrameskip-v4-IMPALA',
    'seed': 0,
    'wandb_logger': {
        'gradient_logger': True,
        'video_logger': True,
        'plot_logger': True,
        'action_logger': True,
        'return_logger': False
    }
}

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Training Procedure

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• Weights & Biases (wandb): monitor link

Model Information

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• Github Repository: repo link
• Doc: DI-engine-docs Algorithm link
• Configuration: config link
• Demo: video

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• Parameters total size: 44894.3 KB
• Last Update Date: 2023-10-20

Environments

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• Benchmark: OpenAI/Gym/Atari
• Task: PongNoFrameskip-v4
• Gym version: 0.25.1
• DI-engine version: v0.4.9
• PyTorch version: 2.0.1+cu117
• Doc: DI-engine-docs Environments link