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Reinforcement Learning (RL)

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Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

코드

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

https://github.com/haje01/distper

Distributed Prioritized Experience Replay

https://github.com/haje01/impala

IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures

https://github.com/halajadallah/RL-Quadcopter_project

Continuous control with deep reinforcement learning

https://github.com/hamidthri/navbot_ppo

Virtual-to-real Deep Reinforcement Learning: Continuous Control of Mobile Robots for Mapless Navigation

https://github.com/hamishs/JAX-RL

Continuous control with deep reinforcement learning

https://github.com/hammer-wang/oml-ppo/blob/master/README.md

Automated Optical Multi-layer Design via Deep Reinforcement Learning

https://github.com/han-cai/PathLevel-EAS

Efficient Architecture Search by Network Transformation

https://github.com/han-won/PlayingAtariWithMindSpore

Playing Atari with Deep Reinforcement Learning

https://github.com/han20192019/newRL

Open Bandit Dataset and Pipeline: Towards Realistic and Reproducible Off-Policy Evaluation

https://github.com/hansungj/BayesianNeuralNetworkDiscretizedGibbsSampler

Weight Uncertainty in Neural Networks

https://github.com/haosulab/ManiSkill-Learn

Off-Policy Deep Reinforcement Learning without Exploration

https://github.com/hardmaru/WorldModelsExperiments

https://github.com/hari-sikchi/loop

Learning Off-Policy with Online Planning

https://github.com/hari-sikchi/pytorch_CPO

Constrained Policy Optimization

https://github.com/haron1100/Upside-Down-Reinforcement-Learning

Reinforcement Learning Upside Down: Don't Predict Rewards -- Just Map Them to Actions

https://github.com/harrybraviner/self_directed_rl

WALL-E: An Efficient Reinforcement Learning Research Framework

https://github.com/harryzhangOG/salved

Safety Augmented Value Estimation from Demonstrations (SAVED): Safe Deep Model-Based RL for Sparse Cost Robotic Tasks

https://github.com/harvitronix/reinforcement-learning-car

Playing Atari with Deep Reinforcement Learning

https://github.com/hbahadirsahin/bayes_by_backprop

Weight Uncertainty in Neural Networks

https://github.com/hbzhang/AwesomeSelfDriving

Reinforcement Learning and Deep Learning based Lateral Control for Autonomous Driving

https://github.com/hcch0912/gym_wrapper_dm_soccer

Arena: a toolkit for Multi-Agent Reinforcement Learning

https://github.com/hdilab/world_model_experiments

https://github.com/hebowei2000/deep-reinforcement-learning

Data-Efficient Hierarchical Reinforcement Learning

https://github.com/hegde95/Agents_that_Listen

Agents that Listen: High-Throughput Reinforcement Learning with Multiple Sensory Systems

https://github.com/hegde95/ViZDoom_with_Sound

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

https://github.com/heidekrueger/bnelearn

Deep Reinforcement Learning from Self-Play in Imperfect-Information Games

https://github.com/helpstonex/crazyara

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

https://github.com/hemilpanchiwala/Dueling-Network-Architectures

Deep Reinforcement Learning with Double Q-learning

https://github.com/hemilpanchiwala/Dueling_Network_Architectures

Deep Reinforcement Learning with Double Q-learning

https://github.com/hemilpanchiwala/Hindsight-Experience-Replay

Continuous control with deep reinforcement learning

https://github.com/hendrycks/test

Aligning AI With Shared Human Values

https://github.com/hengyuan-hu/instruct-rl

Language Instructed Reinforcement Learning for Human-AI Coordination

https://github.com/hepengli/matrpo

Multi-Agent Trust Region Policy Optimization

https://github.com/hepengli/multiagent-particle-envs

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

https://github.com/hetankevin/mdpmix

Model-Free and Model-Based Policy Evaluation when Causality is Uncertain

https://github.com/hex-plex/KiloBot-MultiAgent-RL

Guided Deep Reinforcement Learning for Swarm Systems

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