# reinforcement-learning-algorithms-tensorflow

**Repository Path**: XGX_CURRY_TOM/reinforcement-learning-algorithms-tensorflow

## Basic Information

- **Project Name**: reinforcement-learning-algorithms-tensorflow
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-12-28
- **Last Updated**: 2021-12-28

## Categories & Tags

**Categories**: Uncategorized

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## README

# Reinforcement Learning Algorithms

This repository provides codes for popular Reinforcement Learning algorithms.

All code is written in Python 3 and advanced techniques use [Tensorflow](https://www.tensorflow.org/) for neural network implementations.

#### Table of Contents

- DQN-based
  - DQN ([Mnih et al. 2013](https://arxiv.org/pdf/1312.5602.pdf))
  - DQN with Fixed Q Targets ([Mnih et al. 2013](https://arxiv.org/pdf/1312.5602.pdf))
  - DDQN ([Hasselt et al. 2015](https://arxiv.org/pdf/1509.06461.pdf))
  - DDQN with PER ([Schaul et al. 2016](https://arxiv.org/pdf/1511.05952.pdf))
  - Dueling DDQN ([Wang et al. 2016](http://proceedings.mlr.press/v48/wangf16.pdf))
  - DQN-HER ([Andrychowicz et al. 2018](https://arxiv.org/pdf/1707.01495.pdf))
- Actor Critic
  - A2C
  - A3C ([Mnih et al. 2016](https://arxiv.org/pdf/1602.01783.pdf))
  - DDPG ([Lillicrap et al. 2016](https://arxiv.org/pdf/1509.02971.pdf))
  - TD3 ([Fujimoto et al. 2018](https://arxiv.org/pdf/1802.09477.pdf))
  - SAC ([Haarnoja et al. 2018](https://arxiv.org/pdf/1812.05905.pdf))
  - DDPG-HER ([Andrychowicz et al. 2018](https://arxiv.org/pdf/1707.01495.pdf))
  - SAC-Discrete ([Christodoulou 2019](https://arxiv.org/pdf/1910.07207.pdf))
- Hierarchical
  - h-DQN ([Kulkarni et al. 2016](https://arxiv.org/pdf/1604.06057.pdf))
  - SNN-HRL ([Florensa et al. 2017](https://arxiv.org/pdf/1704.03012.pdf))
  - DIAYN ([Eyensbach et al. 2018](https://arxiv.org/pdf/1802.06070.pdf))
  - HIRO ([Nachum et al. 2018](https://arxiv.org/pdf/1805.08296.pdf))
- Policy Gradient
  - REINFORCE ([Williams et al. 1992](https://people.cs.umass.edu/~barto/courses/cs687/williams92simple.pdf))
  - PPO ([Schulman et al. 2017](https://openai-public.s3-us-west-2.amazonaws.com/blog/2017-07/ppo/ppo-arxiv.pdf))
  
#### References

- [marload/DeepRL-TensorFlow2](https://github.com/marload/DeepRL-TensorFlow2)