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adversarially-motivated-intrinsic-goals

This repository contains code for the method and experiments of the paper "Learning with AMIGo: Adversarially Motivated Intrinsic Goals".

aprl

Efficient Real-World RL for Legged Locomotion via Adaptive Policy Regularization

atari-representation-learning

Code for "Unsupervised State Representation Learning in Atari"

baidu-ocr

百度OCR文字识别API For Node.js

baselines

OpenAI Baselines: high-quality implementations of reinforcement learning algorithms

bear

Code for Stabilizing Off-Policy RL via Bootstrapping Error Reduction

bebold

co

The ultimate generator based flow-control goodness for nodejs (supports thunks, promises, etc)

crazysssst.github.io

deep_abstract_q_network

dvd_es

Code from the paper "Effective Diversity in Population Based Reinforcement Learning", presented as a spotlight at NeurIPS 2020. This is the Evolution Strategies implementation, but of course the method can be used for gradient based RL algorithms (e.g. TD3).

echarts

Enterprise Charts | Github pages : http://ecomfe.github.io/echarts/index-en.html | Email : [email protected] | Baidu Hi : 1379172 |

episodic-policy-minings

gopl-zh

Go语言圣经《The Go Programming Language》中文版!

gym-minigrid

Minimalistic gridworld package for OpenAI Gym

hanoi

汉诺塔(hanoi)演示动画js+html实现

html5media

Enables <video> and <audio> tags in all major browsers.

ife

iv_rl

IV-RL - Sample Efficient Deep Reinforcement Learning via Uncertainty Estimation

made

minimum-baselines

ml-agents

Unity Machine Learning Agents Toolkit

multi-agent-emergence-environments

Environment generation code for the paper "Emergent Tool Use From Multi-Agent Autocurricula"

programmingassignment2

Repository for Programming Assignment 2 for R Programming on Coursera

pymarl

Python Multi-Agent Reinforcement Learning framework

rapid

[ICLR 2021] Rank the Episodes: A Simple Approach for Exploration in Procedurally-Generated Environments.

re3

RE3: State Entropy Maximization with Random Encoders for Efficient Exploration

sac-rnd

Official implementation for "Anti-Exploration by Random Network Distillation", ICML 2023

single-parameter-fit

Real numbers, data science and chaos: How to fit any dataset with a single parameter

softqlearning

Reinforcement Learning with Deep Energy-Based Policies