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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/KshamaDw/collaborative-competitive-games

Generative Adversarial Imitation Learning

https://github.com/Kumar-Tarun/summarization

Self-critical Sequence Training for Image Captioning

https://github.com/Kunal-Kumar-Sahoo/Deep-Reinforcement-Learning-with-Human-Preferences

Deep reinforcement learning from human preferences

https://github.com/KunlinY/darts

DARTS: Differentiable Architecture Search

https://github.com/KunlinY/darts-parallel

DARTS: Differentiable Architecture Search

https://github.com/KyleChen400/patch_dqn

Playing FPS Games with Deep Reinforcement Learning

https://github.com/L0SG/seqgan-music

SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient

https://github.com/LAS1520/Gato-A-Generalist-Agent

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/LKLQQ/LEO

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/LM095/DDPG-implementation

Continuous control with deep reinforcement learning

https://github.com/LMikeH/ocbnn-lmikh

Weight Uncertainty in Neural Networks

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

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

https://github.com/Lagostra/exprimo

Placeto: Learning Generalizable Device Placement Algorithms for Distributed Machine Learning

https://github.com/Laiye-Tech/HDNO

Modelling Hierarchical Structure between Dialogue Policy and Natural Language Generator with Option Framework for Task-oriented Dialogue System

https://github.com/LarreaMikel/bsuite_tutorial

Behaviour Suite for Reinforcement Learning

https://github.com/LauraGentini/QRL

Quantum reinforcement learning

https://github.com/LeejwUniverse/RL_Exploration_Pytorch

Exploration by Random Network Distillation

https://github.com/LeoToledo/PivotingTaskRL

Reinforcement Learning for Pivoting Task

https://github.com/LeoZhengZLY/vertex-net

Safe Reinforcement Learning of Control-Affine Systems with Vertex Networks

https://github.com/Leonardo-H/DR-PG

From Importance Sampling to Doubly Robust Policy Gradient

https://github.com/Lham71/Maximising-Neural-Information-Processing

Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning

https://github.com/LiangqunLu/DLForChatbot

Deep Reinforcement Learning for Dialogue Generation

https://github.com/Liberty3000/rl

Relational Deep Reinforcement Learning

https://github.com/LilTwo/DRL-using-PyTorch

Deep Q-learning from Demonstrations

https://github.com/Linging/Traffic-Signal-Control

Playing Atari with Deep Reinforcement Learning

https://github.com/LiorAl/GymValueIterationNetworks

Value Iteration Networks

https://github.com/Lorna-Liu/ultrasound_vsumm_RL

Ultrasound Video Summarization using Deep Reinforcement Learning

https://github.com/Louiii/ValueDecomposition

Value-Decomposition Networks For Cooperative Multi-Agent Learning

https://github.com/LuK2019/ICNN_BA

Input Convex Neural Networks

https://github.com/LucaMaurici/MultiHop-Reading-Comprehension-DRL

Multi-hop Reading Comprehension via Deep Reinforcement Learning based Document Traversal

https://github.com/LucasAlegre/sac-plus

Soft Actor-Critic Algorithms and Applications

https://github.com/LucasSilve/openAIgym

https://github.com/LukasGardberg/cartpole

Playing Atari with Deep Reinforcement Learning

https://github.com/Lyusungwon/apex_dqn_pytorch

Distributed Prioritized Experience Replay

https://github.com/M46N3/learn2drive

https://github.com/MEOWMEOW114/nd893-p1-navigation-banana

Deep Reinforcement Learning with Double Q-learning

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