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Upload README.md with huggingface_hub

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+ ---
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+ language: en
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+ license: apache-2.0
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+ library_name: pytorch
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+ tags:
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+ - deep-reinforcement-learning
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+ - reinforcement-learning
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+ - DI-engine
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+ - PongNoFrameskip-v4
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+ benchmark_name: OpenAI/Gym/Atari
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+ task_name: PongNoFrameskip-v4
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+ pipeline_tag: reinforcement-learning
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+ model-index:
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+ - name: SampledEfficientZero
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+ results:
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+ - task:
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+ type: reinforcement-learning
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+ name: reinforcement-learning
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+ dataset:
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+ name: PongNoFrameskip-v4
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+ type: PongNoFrameskip-v4
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+ metrics:
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+ - type: mean_reward
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+ value: 21.0 +/- 0.0
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+ name: mean_reward
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+ ---
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+
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+ # Play **PongNoFrameskip-v4** with **SampledEfficientZero** Policy
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+
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+ ## Model Description
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+ <!-- Provide a longer summary of what this model is. -->
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+
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+ This implementation applies **SampledEfficientZero** to the OpenAI/Gym/Atari **PongNoFrameskip-v4** environment using [LightZero](https://github.com/opendilab/LightZero) and [DI-engine](https://github.com/opendilab/di-engine).
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+
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+ **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).
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+
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+ ## Model Usage
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+ ### Install the Dependencies
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+ <details close>
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+ <summary>(Click for Details)</summary>
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+
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+ ```shell
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+ # install huggingface_ding
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+ git clone https://github.com/opendilab/huggingface_ding.git
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+ pip3 install -e ./huggingface_ding/
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+ # install environment dependencies if needed
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+
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+ pip3 install DI-engine[common_env,video]
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+ pip3 install LightZero
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+
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+ ```
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+ </details>
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+
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+ ### Git Clone from Huggingface and Run the Model
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+
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+ <details close>
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+ <summary>(Click for Details)</summary>
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+
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+ ```shell
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+ # running with trained model
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+ python3 -u run.py
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+ ```
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+ **run.py**
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+ ```python
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+ from lzero.agent import SampledEfficientZeroAgent
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+ from ding.config import Config
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+ from easydict import EasyDict
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+ import torch
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+
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+ # Pull model from files which are git cloned from huggingface
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+ policy_state_dict = torch.load("pytorch_model.bin", map_location=torch.device("cpu"))
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+ cfg = EasyDict(Config.file_to_dict("policy_config.py").cfg_dict)
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+ # Instantiate the agent
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+ agent = SampledEfficientZeroAgent(
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+ env_id="PongNoFrameskip-v4", exp_name="PongNoFrameskip-v4-SampledEfficientZero", cfg=cfg.exp_config, policy_state_dict=policy_state_dict
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+ )
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+ # Continue training
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+ agent.train(step=5000)
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+ # Render the new agent performance
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+ agent.deploy(enable_save_replay=True)
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+
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+ ```
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+ </details>
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+
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+ ### Run Model by Using Huggingface_ding
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+
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+ <details close>
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+ <summary>(Click for Details)</summary>
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+
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+ ```shell
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+ # running with trained model
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+ python3 -u run.py
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+ ```
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+ **run.py**
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+ ```python
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+ from lzero.agent import SampledEfficientZeroAgent
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+ from huggingface_ding import pull_model_from_hub
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+
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+ # Pull model from Hugggingface hub
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+ policy_state_dict, cfg = pull_model_from_hub(repo_id="OpenDILabCommunity/PongNoFrameskip-v4-SampledEfficientZero")
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+ # Instantiate the agent
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+ agent = SampledEfficientZeroAgent(
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+ env_id="PongNoFrameskip-v4", exp_name="PongNoFrameskip-v4-SampledEfficientZero", cfg=cfg.exp_config, policy_state_dict=policy_state_dict
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+ )
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+ # Continue training
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+ agent.train(step=5000)
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+ # Render the new agent performance
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+ agent.deploy(enable_save_replay=True)
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+
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+ ```
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+ </details>
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+
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+ ## Model Training
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+
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+ ### Train the Model and Push to Huggingface_hub
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+
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+ <details close>
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+ <summary>(Click for Details)</summary>
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+
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+ ```shell
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+ #Training Your Own Agent
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+ python3 -u train.py
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+ ```
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+ **train.py**
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+ ```python
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+ from lzero.agent import SampledEfficientZeroAgent
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+ from huggingface_ding import push_model_to_hub
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+
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+ # Instantiate the agent
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+ agent = SampledEfficientZeroAgent(env_id="PongNoFrameskip-v4", exp_name="PongNoFrameskip-v4-SampledEfficientZero")
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+ # Train the agent
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+ return_ = agent.train(step=int(2000000))
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+ # Push model to huggingface hub
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+ push_model_to_hub(
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+ agent=agent.best,
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+ env_name="OpenAI/Gym/Atari",
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+ task_name="PongNoFrameskip-v4",
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+ algo_name="SampledEfficientZero",
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+ github_repo_url="https://github.com/opendilab/LightZero",
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+ github_doc_model_url=None,
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+ github_doc_env_url=None,
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+ installation_guide='''
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+ pip3 install DI-engine[common_env,video]
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+ pip3 install LightZero
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+ ''',
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+ usage_file_by_git_clone="./sampled_efficientzero/pong_sampled_efficientzero_deploy.py",
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+ usage_file_by_huggingface_ding="./sampled_efficientzero/pong_sampled_efficientzero_download.py",
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+ train_file="./sampled_efficientzero/pong_sampled_efficientzero.py",
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+ repo_id="OpenDILabCommunity/PongNoFrameskip-v4-SampledEfficientZero",
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+ platform_info="[LightZero](https://github.com/opendilab/LightZero) and [DI-engine](https://github.com/opendilab/di-engine)",
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+ 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).",
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+ create_repo=True
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+ )
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+
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+ ```
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+ </details>
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+
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+ **Configuration**
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+ <details close>
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+ <summary>(Click for Details)</summary>
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+
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+
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+ ```python
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+ exp_config = {
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+ 'main_config': {
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+ 'exp_name': 'PongNoFrameskip-v4-SampledEfficientZero',
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+ 'seed': 0,
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+ 'env': {
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+ 'env_id': 'PongNoFrameskip-v4',
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+ 'env_name': 'PongNoFrameskip-v4',
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+ 'obs_shape': [4, 96, 96],
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+ 'collector_env_num': 8,
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+ 'evaluator_env_num': 3,
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+ 'n_evaluator_episode': 3,
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+ 'manager': {
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+ 'shared_memory': False
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+ }
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+ },
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+ 'policy': {
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+ 'on_policy': False,
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+ 'cuda': True,
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+ 'multi_gpu': False,
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+ 'bp_update_sync': True,
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+ 'traj_len_inf': False,
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+ 'model': {
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+ 'observation_shape': [4, 96, 96],
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+ 'frame_stack_num': 4,
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+ 'action_space_size': 6,
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+ 'downsample': True,
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+ 'continuous_action_space': False,
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+ 'num_of_sampled_actions': 5,
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+ 'discrete_action_encoding_type': 'one_hot',
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+ 'norm_type': 'BN'
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+ },
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+ 'use_rnd_model': False,
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+ 'sampled_algo': True,
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+ 'gumbel_algo': False,
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+ 'mcts_ctree': True,
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+ 'collector_env_num': 8,
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+ 'evaluator_env_num': 3,
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+ 'env_type': 'not_board_games',
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+ 'action_type': 'fixed_action_space',
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+ 'battle_mode': 'play_with_bot_mode',
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+ 'monitor_extra_statistics': True,
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+ 'game_segment_length': 400,
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+ 'transform2string': False,
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+ 'gray_scale': False,
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+ 'use_augmentation': True,
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+ 'augmentation': ['shift', 'intensity'],
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+ 'ignore_done': False,
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+ 'update_per_collect': 1000,
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+ 'model_update_ratio': 0.1,
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+ 'batch_size': 256,
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+ 'optim_type': 'SGD',
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+ 'learning_rate': 0.2,
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+ 'target_update_freq': 100,
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+ 'target_update_freq_for_intrinsic_reward': 1000,
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+ 'weight_decay': 0.0001,
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+ 'momentum': 0.9,
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+ 'grad_clip_value': 10,
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+ 'n_episode': 8,
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+ 'num_simulations': 50,
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+ 'discount_factor': 0.997,
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+ 'td_steps': 5,
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+ 'num_unroll_steps': 5,
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+ 'reward_loss_weight': 1,
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+ 'value_loss_weight': 0.25,
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+ 'policy_loss_weight': 1,
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+ 'policy_entropy_loss_weight': 0,
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+ 'ssl_loss_weight': 2,
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+ 'lr_piecewise_constant_decay': True,
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+ 'threshold_training_steps_for_final_lr': 50000,
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+ 'manual_temperature_decay': False,
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+ 'threshold_training_steps_for_final_temperature': 100000,
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+ 'fixed_temperature_value': 0.25,
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+ 'use_ture_chance_label_in_chance_encoder': False,
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+ 'use_priority': True,
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+ 'priority_prob_alpha': 0.6,
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+ 'priority_prob_beta': 0.4,
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+ 'root_dirichlet_alpha': 0.3,
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+ 'root_noise_weight': 0.25,
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+ 'random_collect_episode_num': 0,
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+ 'eps': {
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+ 'eps_greedy_exploration_in_collect': False,
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+ 'type': 'linear',
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+ 'start': 1.0,
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+ 'end': 0.05,
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+ 'decay': 100000
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+ },
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+ 'cfg_type': 'SampledEfficientZeroPolicyDict',
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+ 'init_w': 0.003,
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+ 'normalize_prob_of_sampled_actions': False,
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+ 'policy_loss_type': 'cross_entropy',
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+ 'lstm_horizon_len': 5,
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+ 'cos_lr_scheduler': False,
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+ 'reanalyze_ratio': 0.0,
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+ 'eval_freq': 2000,
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+ 'replay_buffer_size': 1000000
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+ },
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+ 'wandb_logger': {
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+ 'gradient_logger': False,
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+ 'video_logger': False,
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+ 'plot_logger': False,
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+ 'action_logger': False,
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+ 'return_logger': False
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+ }
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+ },
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+ 'create_config': {
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+ 'env': {
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+ 'type': 'atari_lightzero',
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+ 'import_names': ['zoo.atari.envs.atari_lightzero_env']
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+ },
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+ 'env_manager': {
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+ 'type': 'subprocess'
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+ },
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+ 'policy': {
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+ 'type': 'sampled_efficientzero',
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+ 'import_names': ['lzero.policy.sampled_efficientzero']
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+ }
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+ }
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+ }
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+
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+ ```
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+ </details>
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+
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+ **Training Procedure**
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+ <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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+ - **Weights & Biases (wandb):** [monitor link](<TODO>)
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+
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+ ## Model Information
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+ <!-- Provide the basic links for the model. -->
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+ - **Github Repository:** [repo link](https://github.com/opendilab/LightZero)
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+ - **Doc**: [Algorithm link](<TODO>)
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+ - **Configuration:** [config link](https://huggingface.co/OpenDILabCommunity/PongNoFrameskip-v4-SampledEfficientZero/blob/main/policy_config.py)
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+ - **Demo:** [video](https://huggingface.co/OpenDILabCommunity/PongNoFrameskip-v4-SampledEfficientZero/blob/main/replay.mp4)
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+ <!-- Provide the size information for the model. -->
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+ - **Parameters total size:** 33023.14 KB
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+ - **Last Update Date:** 2024-01-19
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+
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+ ## Environments
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+ <!-- Address questions around what environment the model is intended to be trained and deployed at, including the necessary information needed to be provided for future users. -->
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+ - **Benchmark:** OpenAI/Gym/Atari
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+ - **Task:** PongNoFrameskip-v4
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+ - **Gym version:** 0.25.1
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+ - **DI-engine version:** v0.5.0
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+ - **PyTorch version:** 2.0.1+cu117
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+ - **Doc**: [Environments link](<TODO>)