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Reinforcement Learning

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코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/caslab-vt/SARNet

Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments

https://github.com/cassieqiuyd/MJOLNIR

Learning hierarchical relationships for object-goal navigation

https://github.com/cataluna84/WorldModels_v1.0

World Models

https://github.com/catalyst-team/catalyst-rl-framework

Catalyst.RL: A Distributed Framework for Reproducible RL Research

https://github.com/cathyhxh/ctds

Value-Decomposition Networks For Cooperative Multi-Agent Learning

https://github.com/cathywu/rllab-multiagent

Benchmarking Deep Reinforcement Learning for Continuous Control

https://github.com/caymansimpson/reuniclusVGC

Game-Theoretic Multiagent Reinforcement Learning

https://github.com/cbellinger27/adaptive_optics_gym

OpenAI Gym

https://github.com/cbellinger27/semi-MarkovRLForMatrials

Reinforcement Learning in a Physics-Inspired Semi-Markov Environment

https://github.com/cbg-ethz/bmi

Representation Learning with Contrastive Predictive Coding

https://github.com/cbschaff/nlimb

Jointly Learning to Construct and Control Agents using Deep Reinforcement Learning

https://github.com/cda-tum/mqt-predictor

Compiler Optimization for Quantum Computing Using Reinforcement Learning

https://github.com/cdebeunne/uncertainties_CNN

Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

https://github.com/cdesilv1/sc2_ai_cdes

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/cejuicreamice/R_easy_RL_fix

Amplifying the Imitation Effect for Reinforcement Learning of UCAV's Mission Execution

https://github.com/cesch97/NeuroEvolution

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/chaeger/upper_capacity_bounds

Deep Learning for Channel Coding via Neural Mutual Information Estimation

https://github.com/chaitjo/graph-convnet-tsp

On Learning Paradigms for the Travelling Salesman Problem

https://github.com/chaitjo/learning-tsp

Neural Combinatorial Optimization with Reinforcement Learning

https://github.com/chamorajg/pl-dreamer

Dream to Control: Learning Behaviors by Latent Imagination

https://github.com/chandar-lab/RLHive

Playing Atari with Deep Reinforcement Learning

https://github.com/chandu-97/BayesByBackprop

Weight Uncertainty in Neural Networks

https://github.com/chaneeh/SeqGAN_experiment

SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient

https://github.com/changpowei/MicroRTS

Comparing Observation and Action Representations for Deep Reinforcement Learning in $μ$RTS

https://github.com/chanlaw/assistive-bandits

Cooperative Inverse Reinforcement Learning

https://github.com/chariff/GPro

A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning

https://github.com/charlenekok/Unity-Technologies-Machine-Learning

Unity: A General Platform for Intelligent Agents

https://github.com/charleshsc/qt

Q-value Regularized Transformer for Offline Reinforcement Learning

https://github.com/chauncygu/safe-multi-agent-isaac-gym

Multi-Agent Constrained Policy Optimisation

https://github.com/chauncygu/safe-multi-agent-mujoco

Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning

https://github.com/chauncygu/safe-multi-agent-robosuite

Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning

https://github.com/chenbys/GradCAM

Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization

https://github.com/chengyu2/vizdoom_rl_community_canberra

ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning

https://github.com/chenh1001/Basketball-ML-Unity

Unity: A General Platform for Intelligent Agents

https://github.com/chenhongge/SA_DQN

Whatever Does Not Kill Deep Reinforcement Learning, Makes It Stronger

https://github.com/chinesh/Guided_Grad_CAM

Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization

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