https://github.com/ajaysub110/rlin200lines

PyTorch implementations of Reinforcement Learning algorithms in less than 200 lines
https://github.com/ajaysub110/rlin200lines

deep-reinforcement-learning dqn machine-learning policy-gradient ppo pytorch-implementations reinforcement-learning reinforcement-learning-algorithms soft-actor-critic

Last synced: 28 days ago
JSON representation

PyTorch implementations of Reinforcement Learning algorithms in less than 200 lines

• Host: GitHub
• URL: https://github.com/ajaysub110/rlin200lines
• Owner: ajaysub110
• Created: 2019-04-20T17:40:21.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2020-04-03T14:00:58.000Z (almost 5 years ago)
• Last Synced: 2024-11-12T20:22:41.887Z (about 2 months ago)
• Topics: deep-reinforcement-learning, dqn, machine-learning, policy-gradient, ppo, pytorch-implementations, reinforcement-learning, reinforcement-learning-algorithms, soft-actor-critic
• Language: Python
• Homepage:
• Size: 2.98 MB
• Stars: 10
• Watchers: 2
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# RL in 200 Lines

PyTorch implementations of Reinforcement Learning algorithms in less than 200 lines.

## Algorithms:

1. **Deep Reinforcement Learning**

    - DQN

    - Soft Actor-Critic (SAC) [[Results]](https://drive.google.com/open?id=1rrzC4DigBsKWXv9YVmV0jI1vhstnwFKd)

    - Vanilla Policy Gradient (Actor-Critic) [[Results]](https://drive.google.com/open?id=1T9rqRIfZcCe61h2Ib-Q9Fyf2A-B9th2A)

    - Proximal Policy Optimization (PPO) [[Results]](https://drive.google.com/open?id=1zb6bt5RSZUnCTRw8crWPypXHkiECyRG4)

    - Deep Deterministic Policy Gradient (DDPG) [[Results]](https://drive.google.com/open?id=1IS06f5od8-mNvi9oaSlIOqFZoxJpdNcx)

2. **Bandits**

    - Epsilon Greedy

    - Softmax action selection

    - UCB-1

    - REINFORCE

3. **Classical MDP Control**

    - SARSA

    - Q-learning

    - SARSA(lambda)

    - Vanilla Policy Gradient

4. **Additional Resources**

    - Report on Bandit algorithms

    - Report on Classical MDP control algorithms

    - Contour environment - *gym-contour* 

    - Puddle world - *gym-puddle*

## Dependencies

- PyTorch

- Tensorboard

- OpenAI Gym

- Numpy

## Usage

- Clone the repository.

- Run experiments on an algorithm by running either .py or main.py within its directory.

- Tensorboard of my experiments can be viewed by using the 'Result' links given above.

## References

- **Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor**, (2018) [[bib]](./bibtex.bib#L9-L15)  by *Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel and Sergey Levine*

- **Proximal Policy Optimization Algorithms**, (2017) [[bib]](./bibtex.bib#L25-L31)  by *John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford and Oleg Klimov*

- **Benchmarking Deep Reinforcement Learning for Continuous Control**, (2016) [[bib]](./bibtex.bib#L17-L23)  by *Yan Duan, Xi Chen, Rein Houthooft, John Schulman and Pieter Abbeel*

- **Playing Atari with Deep Reinforcement Learning**, (2013) [[bib]](./bibtex.bib#L1-L7)  by *Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra and Martin A. Riedmiller*

- **Using Confidence Bounds for Exploitation-Exploration Trade-offs**, (2002) [[bib]](./bibtex.bib#L42-L49)  by *Peter Auer*

- **Eligibility Traces for Off-Policy Policy Evaluation**, (2000) [[bib]](./bibtex.bib#L60-L65)  by *Doina Precup, Richard S. Sutton and Satinder P. Singh*

- **Policy Gradient Methods for Reinforcement Learning with Function Approximation**, (1999) [[bib]](./bibtex.bib#L67-L72)  by *Richard S. Sutton, David A. McAllester, Satinder P. Singh and Yishay Mansour*

- **Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning**, (1992) [[bib]](./bibtex.bib#L33-L40)  by *Ronald J. Williams*

- **Q-learning**, (1992) [[bib]](./bibtex.bib#L51-L58)  by *Chris Watkins and Peter Dayan*

- **Deterministic Policy Gradient Algorithms**, (2014) [[bib]](../bibtex.bib#L74-L79)  by *David Silver, Guy Lever, Nicolas Manfred Otto Heess, Thomas Degris, Daan Wierstra and Martin A. Riedmiller*