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	---
	language: en
	license: apache-2.0
	library_name: pytorch
	tags:
	- deep-reinforcement-learning
	- reinforcement-learning
	- DI-engine
	- SpaceInvadersNoFrameskip-v4
	benchmark_name: OpenAI/Gym/Atari
	task_name: SpaceInvadersNoFrameskip-v4
	pipeline_tag: reinforcement-learning
	model-index:
	- name: C51
	results:
	- task:
	type: reinforcement-learning
	name: reinforcement-learning
	dataset:
	name: OpenAI/Gym/Atari-SpaceInvadersNoFrameskip-v4
	type: OpenAI/Gym/Atari-SpaceInvadersNoFrameskip-v4
	metrics:
	- type: mean_reward
	value: 1453.75 +/- 947.72
	name: mean_reward
	---

	# Play SpaceInvadersNoFrameskip-v4 with C51 Policy

	## Model Description
	<!-- Provide a longer summary of what this model is. -->
	This is a simple C51 implementation to OpenAI/Gym/Atari SpaceInvadersNoFrameskip-v4 using the [DI-engine library](https://github.com/opendilab/di-engine) and the [DI-zoo](https://github.com/opendilab/DI-engine/tree/main/dizoo).

	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
	<details close>
	<summary>(Click for Details)</summary>

	```shell
	# 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]
	```
	</details>

	### Git Clone from Huggingface and Run the Model

	<details close>
	<summary>(Click for Details)</summary>

	```shell
	# running with trained model
	python3 -u run.py
	```
	run.py
	```python
	from ding.bonus import C51Agent
	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"))
	# Instantiate the agent
	agent = C51Agent(
	env="SpaceInvadersNoFrameskip", exp_name="SpaceInvadersNoFrameskip-v4-C51", 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)

	```
	</details>

	### Run Model by Using Huggingface_ding

	<details close>
	<summary>(Click for Details)</summary>

	```shell
	# running with trained model
	python3 -u run.py
	```
	run.py
	```python
	from ding.bonus import C51Agent
	from huggingface_ding import pull_model_from_hub

	# Pull model from Hugggingface hub
	policy_state_dict, cfg = pull_model_from_hub(repo_id="OpenDILabCommunity/SpaceInvadersNoFrameskip-v4-C51")
	# Instantiate the agent
	agent = C51Agent(
	env="SpaceInvadersNoFrameskip", exp_name="SpaceInvadersNoFrameskip-v4-C51", 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)

	```
	</details>

	## Model Training

	### Train the Model and Push to Huggingface_hub

	<details close>
	<summary>(Click for Details)</summary>

	```shell
	#Training Your Own Agent
	python3 -u train.py
	```
	train.py
	```python
	from ding.bonus import C51Agent
	from huggingface_ding import push_model_to_hub

	# Instantiate the agent
	agent = C51Agent(env="SpaceInvadersNoFrameskip", exp_name="SpaceInvadersNoFrameskip-v4-C51")
	# 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="SpaceInvadersNoFrameskip-v4",
	algo_name="C51",
	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/c51.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]",
	usage_file_by_git_clone="./c51/spaceinvaders_c51_deploy.py",
	usage_file_by_huggingface_ding="./c51/spaceinvaders_c51_download.py",
	train_file="./c51/spaceinvaders_c51.py",
	repo_id="OpenDILabCommunity/SpaceInvadersNoFrameskip-v4-C51"
	)

	```
	</details>

	Configuration
	<details close>
	<summary>(Click for Details)</summary>


	```python
	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',
	'shared_memory': False
	},
	'stop_value': 10000000000,
	'n_evaluator_episode': 8,
	'collector_env_num': 8,
	'evaluator_env_num': 8,
	'env_id': 'SpaceInvadersNoFrameskip-v4',
	'frame_stack': 4
	},
	'policy': {
	'model': {
	'encoder_hidden_size_list': [128, 128, 512],
	'v_min': -10,
	'v_max': 10,
	'n_atom': 51,
	'obs_shape': [4, 84, 84],
	'action_shape': 6
	},
	'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': 10,
	'batch_size': 32,
	'learning_rate': 0.0001,
	'target_update_freq': 500,
	'target_theta': 0.005,
	'ignore_done': False
	},
	'collect': {
	'collector': {},
	'n_sample': 100,
	'unroll_len': 1
	},
	'eval': {
	'evaluator': {
	'eval_freq': 4000,
	'render': {
	'render_freq': -1,
	'mode': 'train_iter'
	},
	'cfg_type': 'InteractionSerialEvaluatorDict',
	'stop_value': 10000000000,
	'n_episode': 8
	}
	},
	'other': {
	'replay_buffer': {
	'replay_buffer_size': 400000
	},
	'eps': {
	'type': 'exp',
	'start': 1.0,
	'end': 0.05,
	'decay': 1000000
	}
	},
	'on_policy': False,
	'cuda': True,
	'multi_gpu': False,
	'bp_update_sync': True,
	'traj_len_inf': False,
	'type': 'c51',
	'priority': False,
	'priority_IS_weight': False,
	'discount_factor': 0.99,
	'nstep': 3,
	'cfg_type': 'C51PolicyDict'
	},
	'exp_name': 'SpaceInvadersNoFrameskip-v4-C51',
	'seed': 0,
	'wandb_logger': {
	'gradient_logger': True,
	'video_logger': True,
	'plot_logger': True,
	'action_logger': True,
	'return_logger': False
	}
	}

	```
	</details>

	Training Procedure
	<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
	- Weights & Biases (wandb): [monitor link](https://wandb.ai/zjowowen/SpaceInvadersNoFrameskip-v4-C51)

	## Model Information
	<!-- Provide the basic links for the model. -->
	- Github Repository: [repo link](https://github.com/opendilab/DI-engine)
	- Doc: [DI-engine-docs Algorithm link](https://di-engine-docs.readthedocs.io/en/latest/12_policies/c51.html)
	- Configuration: [config link](https://huggingface.co/OpenDILabCommunity/SpaceInvadersNoFrameskip-v4-C51/blob/main/policy_config.py)
	- Demo: [video](https://huggingface.co/OpenDILabCommunity/SpaceInvadersNoFrameskip-v4-C51/blob/main/replay.mp4)
	<!-- Provide the size information for the model. -->
	- Parameters total size: 55276.2 KB
	- Last Update Date: 2023-05-18

	## Environments
	<!-- 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. -->
	- Benchmark: OpenAI/Gym/Atari
	- Task: SpaceInvadersNoFrameskip-v4
	- Gym version: 0.25.1
	- DI-engine version: v0.4.7
	- PyTorch version: 1.7.1
	- Doc: [DI-engine-docs Environments link](https://di-engine-docs.readthedocs.io/en/latest/13_envs/atari.html)