TransformerXL + PPO Baseline + MemoryGym

• episodic-transformer-memory-ppo

  5 106 0.0 Python

  Clean baseline implementation of PPO using an episodic TransformerXL memory

We finally completed a lightweight implementation of a memory-based agent using PPO and TransformerXL (and Gated TransformerXL).

• brain-agent

  2 92 3.0 Python

  Brain Agent for Large-Scale and Multi-Task Agent Learning

  

Brain Agent

• WorkOS

  workos.com sponsored

  The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.

  

• DI-engine

  3 2,486 8.8 Python

  OpenDILab Decision AI Engine

  

DI Engine

• Ray

  42 30,988 10.0 Python

  Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

  

RLlib

• endless-memory-gym

  1 66 7.7 Python

  Challenging Memory-based Deep Reinforcement Learning Agents

Code: https://github.com/MarcoMeter/drl-memory-gym

• adaptive-transformers-in-rl

  1 126 10.0 Python

  Adaptive Attention Span for Reinforcement Learning

Found relevant code at https://github.com/jerrodparker20/adaptive-transformers-in-rl + all code implementations here

• popgym

  4 142 6.1 Python

  Partially Observable Process Gym

  

Have you seen this other ICLR paper, POPGym? Paper: https://openreview.net/forum?id=chDrutUTs0K Code: https://github.com/smorad/popgym

• InfluxDB

  www.influxdata.com sponsored

  Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

  

• Gymnasium

  12 5,651 9.3 Python

  An API standard for single-agent reinforcement learning environments, with popular reference environments and related utilities (formerly Gym)

  

Thanks! It really depends on the task that you want to implement. But in general, sticking to the standard gymnasium API is important. If you want to implement a 2D environment then PyGame is promising. If it's more like a game, check out Unity ML-Agents or Godot RL Agents. Anything simpler can also be just pure python code. You also need to carefully design your observation space, action space and reward function. My advice is to explore design choices of related environments.

• ml-agents

  60 16,295 8.1 C#

  The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents using deep reinforcement learning and imitation learning.

  

Thanks! It really depends on the task that you want to implement. But in general, sticking to the standard gymnasium API is important. If you want to implement a 2D environment then PyGame is promising. If it's more like a game, check out Unity ML-Agents or Godot RL Agents. Anything simpler can also be just pure python code. You also need to carefully design your observation space, action space and reward function. My advice is to explore design choices of related environments.

• godot_rl_agents

  5 737 9.2 Python

  An Open Source package that allows video game creators, AI researchers and hobbyists the opportunity to learn complex behaviors for their Non Player Characters or agents

Thanks! It really depends on the task that you want to implement. But in general, sticking to the standard gymnasium API is important. If you want to implement a 2D environment then PyGame is promising. If it's more like a game, check out Unity ML-Agents or Godot RL Agents. Anything simpler can also be just pure python code. You also need to carefully design your observation space, action space and reward function. My advice is to explore design choices of related environments.

Suggest a related project