Pinned Repositories
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
CrazySssst's Repositories
CrazySssst/adversarially-motivated-intrinsic-goals
This repository contains code for the method and experiments of the paper "Learning with AMIGo: Adversarially Motivated Intrinsic Goals".
CrazySssst/APRL
Efficient Real-World RL for Legged Locomotion via Adaptive Policy Regularization
CrazySssst/atari-representation-learning
Code for "Unsupervised State Representation Learning in Atari"
CrazySssst/baselines
OpenAI Baselines: high-quality implementations of reinforcement learning algorithms
CrazySssst/BEAR
Code for Stabilizing Off-Policy RL via Bootstrapping Error Reduction
CrazySssst/bebold
CrazySssst/co
The ultimate generator based flow-control goodness for nodejs (supports thunks, promises, etc)
CrazySssst/CrazySssst.github.io
CrazySssst/deep_abstract_q_network
CrazySssst/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).
CrazySssst/echarts
Enterprise Charts | Github pages : http://ecomfe.github.io/echarts/index-en.html | Email : echarts@baidu.com | Baidu Hi : 1379172 |
CrazySssst/episodic-policy-minings
CrazySssst/gopl-zh
Go语言圣经《The Go Programming Language》中文版!
CrazySssst/gym-minigrid
Minimalistic gridworld package for OpenAI Gym
CrazySssst/ife
CrazySssst/iv_rl
IV-RL - Sample Efficient Deep Reinforcement Learning via Uncertainty Estimation
CrazySssst/MADE
CrazySssst/minimum-baselines
CrazySssst/ml-agents
Unity Machine Learning Agents Toolkit
CrazySssst/multi-agent-emergence-environments
Environment generation code for the paper "Emergent Tool Use From Multi-Agent Autocurricula"
CrazySssst/ProgrammingAssignment2
Repository for Programming Assignment 2 for R Programming on Coursera
CrazySssst/pymarl
Python Multi-Agent Reinforcement Learning framework
CrazySssst/rapid
[ICLR 2021] Rank the Episodes: A Simple Approach for Exploration in Procedurally-Generated Environments.
CrazySssst/RE3
RE3: State Entropy Maximization with Random Encoders for Efficient Exploration
CrazySssst/sac-rnd
Official implementation for "Anti-Exploration by Random Network Distillation", ICML 2023
CrazySssst/single-parameter-fit
Real numbers, data science and chaos: How to fit any dataset with a single parameter
CrazySssst/softqlearning
Reinforcement Learning with Deep Energy-Based Policies
CrazySssst/tomorrow-theme
Tomorrow Theme the precursor to Base16 Theme
CrazySssst/TradeMaster
TradeMaster is an open-source platform for quantitative trading empowered by reinforcement learning :fire: :zap: :rainbow:
CrazySssst/vue
Simple yet powerful library for building modern web interfaces.