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agents

Reproducible results for the various types of reinforcement algorithms I have implemented

https://github.com/beegass/agents

Science Score: 67.0%

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    Links to: arxiv.org
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    Low similarity (7.5%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

Reproducible results for the various types of reinforcement algorithms I have implemented

Basic Info
  • Host: GitHub
  • Owner: BeeGass
  • License: other
  • Language: Python
  • Default Branch: master
  • Homepage:
  • Size: 6.72 MB
Statistics
  • Stars: 2
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created over 4 years ago · Last pushed almost 4 years ago
Metadata Files
Readme License Citation

README.md

RL Agents
Jax | Flux | PyTorch

A collection of Reinforcement Learning (RL) Methods I have implemented in jax/flax, flux and pytorch with particular effort put into readability and reproducibility.

Python

Requirements For Jax

  • Python >= 3.8
  • jax

Installation

$ git clone https://github.com/BeeGass/Agents.git

Usage

$ cd Agents/agents-jax $ python main.py

Requirements For PyTorch

  • PyTorch >= 1.10

Usage

$ cd Agents/agents-pytorch $ python main.py

Julia

Requirements For Flux

  • TODO
  • TODO

Usage

``` $ cd Agents/agents-flux $ # TBA

```

Config File Template yaml TBA

Weights And Biases Integration TBA

Preliminary RL Implementations

| Model | NumPy/Vanilla | Jax/Flax | Flux | Config | Paper | |:-------------------------------------------- |:-------------:|:--------:|:-------:|:-------:|:-------------------------------------------------------------------------------------------- | | Policy Evaluation | ☑ | ☐ | ☐ | ☐ | DS595-RL-Projects | | Policy Improvement | ☑ | ☐ | ☐ | ☐ | DS595-RL-Projects | | Policy Iteration | ☑ | ☐ | ☐ | ☐ | DS595-RL-Projects | | Value Iteration | ☑ | ☐ | ☐ | ☐ | DS595-RL-Projects | | On-policy first visit Monte-Carlo prediction | ☑ | ☐ | ☐ | ☐ | DS595-RL-Projects | | On-policy first visit Monte-Carlo control | ☑ | ☐ | ☐ | ☐ | DS595-RL-Projects | | Sarsa (on-policy TD control) | ☑ | ☐ | ☐ | ☐ | DS595-RL-Projects | | Q-learing (off-policy TD control) | ☑ | ☐ | ☐ | ☐ | DS595-RL-Projects |

Off-Policy Results

| Model | PyTorch | Jax/Flax | Flux | Config | Paper | |:----------- |:-------:|:--------:|:-------:|:-------:|:------------------------------------------ | | DQN | ☐ | ☐ | ☐ | ☐ | Link | | DDPG | ☐ | ☐ | ☐ | ☐ | Link | | DRQN | ☐ | ☐ | ☐ | ☐ | Link | | Dueling-DQN | ☐ | ☐ | ☐ | ☐ | Link | | Double-DQN | ☐ | ☐ | ☐ | ☐ | Link | | PER | ☐ | ☐ | ☐ | ☐ | Link | | Rainbow | ☐ | ☐ | ☐ | ☐ | Link |

Policy Results

| Model | PyTorch | Jax/Flax | Flux | Config | Paper | |:----- |:-------:|:--------:|:-------:|:-------:|:---------------------------------------- | | PPO | ☐ | ☐ | ☐ | ☐ | Link | | TRPO | ☐ | ☐ | ☐ | ☐ | Link | | SAC | ☐ | ☐ | ☐ | ☐ | Link | | A2C | ☐ | ☐ | ☐ | ☐ | Link | | A3C | ☐ | ☐ | ☐ | ☐ | Link | | TD3 | ☐ | ☐ | ☐ | ☐ | Link |

Fun Stuff

| Model | PyTorch | Jax/Flax | Flux | Config | Paper | |:------------------- |:-------:|:--------:|:-------:|:-------:|:-------------------------------------------- | | World Models | ☐ | ☐ | ☐ | ☐ | Link | | Dream to Control | ☐ | ☐ | ☐ | ☐ | Link | | Dream to Control v2 | ☐ | ☐ | ☐ | ☐ | Link |

Citation

bib @software{Gass_Agents_2021, author = {Gass, B.A., Gass, B.A.}, doi = {10.5281/zenodo.1234}, month = {12}, title = {{Agents}}, url = {https://github.com/BeeGass/Agents}, version = {1.0.0}, year = {2021} }

Owner

  • Name: Bryan
  • Login: BeeGass
  • Kind: user
  • Location: Cambridge, MA
  • Company: @USArmyResearchLab

Research Engineer interested in SSMs

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: "Gass, B.A."
  given-names: "Gass, B.A."
  orcid: "https://orcid.org/0000-0000-0000-0000"
title: "Agents"
version: 1.0.0
doi: 10.5281/zenodo.1234
date-released: 2021-12-06
url: "https://github.com/BeeGass/Agents"

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