OpenDILab Decision AI Engine. The Most Comprehensive Reinforcement Learning Framework B.P.
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Updated
Nov 5, 2024 - Python
OpenDILab Decision AI Engine. The Most Comprehensive Reinforcement Learning Framework B.P.
Reinforcement Learning Coach by Intel AI Lab enables easy experimentation with state of the art Reinforcement Learning algorithms
Unified Reinforcement Learning Framework
🏛️A research-friendly codebase for fast experimentation of single-agent reinforcement learning in JAX • End-to-End JAX RL
Use AWS RoboMaker and demonstrate running a simulation which trains a reinforcement learning (RL) model to drive a car around a track
A framework for easy prototyping of distributed reinforcement learning algorithms
Scalable distributed reinforcement learning agents on kubernetes
implementation of distributed reinforcement learning with distributed tensorflow
Code accompanying the paper "TiZero: Mastering Multi-Agent Football with Curriculum Learning and Self-Play" (AAMAS 2023) 足球游戏智能体
Use AWS RoboMaker and demonstrate a simulation that can train a reinforcement learning model to make a TurtleBot WafflePi to follow a TurtleBot burger, and then Deploy via RoboMaker to the robot.
[NeurIPS 2022] DMAP: a Distributed Morphological Attention Policy for Learning to Locomote with a Changing Body
A PyTorch implementation of reinforcement lerning algorithms (DQN, DDQN, Prior DDQN, Distributed) based on ray
Distributed RL platform with modified IMPALA architecture. Implements CLEAR, LASER V-trace modifications along with Attentive and Elite sampling experience replay methods.
This repository contains the implementation of a wide variety of Reinforcement Learning Projects in different applications of Bandit Algorithms, MDPs, Distributed RL and Deep RL. These projects include university projects and projects implemented due to interest in Reinforcement Learning.
Distributed Deep Reinforcement Learning Framework
reinforcement learning alogrithm implement with Ray
Implementation of certain crucial algorithms in the field of reinforcement learning.
Minimal implementations of distributed, recurrent, deep reinforcement learning algorithms
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