---
language: en
license: apache-2.0
library_name: pytorch
tags:
- deep-reinforcement-learning
- reinforcement-learning
- DI-engine
- QbertNoFrameskip-v4
benchmark_name: OpenAI/Gym/Atari
task_name: QbertNoFrameskip-v4
pipeline_tag: reinforcement-learning
model-index:
- name: DQN
  results:
  - task:
      type: reinforcement-learning
      name: reinforcement-learning
    dataset:
      name: OpenAI/Gym/Atari-QbertNoFrameskip-v4
      type: OpenAI/Gym/Atari-QbertNoFrameskip-v4
    metrics:
    - type: mean_reward
      value: 16375.0 +/- 0.0
      name: mean_reward
---

# Play **QbertNoFrameskip-v4** with **DQN** Policy

## Model Description
<!-- Provide a longer summary of what this model is. -->
This is a simple **DQN** implementation to OpenAI/Gym/Atari **QbertNoFrameskip-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
# [More Information Needed]
```
</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
# [More Information Needed]
```
</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
# [More Information Needed]
```
</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'
        },
        'stop_value': 30000,
        'n_evaluator_episode': 8,
        'env_id': 'QbertNoFrameskip-v4',
        'collector_env_num': 8,
        'evaluator_env_num': 8,
        'fram_stack': 4,
        'env_wrapper': 'atari_default'
    },
    'policy': {
        'model': {
            'encoder_hidden_size_list': [128, 128, 512],
            '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': 1000,
                'render': {
                    'render_freq': -1,
                    'mode': 'train_iter'
                },
                'figure_path': None,
                'cfg_type': 'InteractionSerialEvaluatorDict',
                'stop_value': 30000,
                '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': 'dqn',
        'priority': False,
        'priority_IS_weight': False,
        'discount_factor': 0.99,
        'nstep': 3,
        'cfg_type': 'DQNPolicyDict'
    },
    'exp_name': 'QbertNoFrameskip-v4-DQN',
    '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/QbertNoFrameskip-v4-DQN)

## 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/dqn.html)
- **Configuration:** [config link](https://huggingface.co/OpenDILabCommunity/QbertNoFrameskip-v4-DQN/blob/main/policy_config.py)
- **Demo:** [video](https://huggingface.co/OpenDILabCommunity/QbertNoFrameskip-v4-DQN/blob/main/replay.mp4)
<!-- Provide the size information for the model. -->
- **Parameters total size:** 55703.03 KB
- **Last Update Date:** 2023-07-23

## 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:** QbertNoFrameskip-v4
- **Gym version:** 0.25.1
- **DI-engine version:** v0.4.8
- **PyTorch version:** 2.0.1+cu117
- **Doc**: [DI-engine-docs Environments link](https://di-engine-docs.readthedocs.io/en/latest/13_envs/atari.html)