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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/ythuangyt/Robust-Reinforcement-Learning-via-Adversarial-training-with-Langevin-Dynamics

Robust Reinforcement Learning via Adversarial training with Langevin Dynamics

https://github.com/yuanfeisiyuetian/seqgan-modbusTCP

SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient

https://github.com/yuanming-hu/exposure

Exposure: A White-Box Photo Post-Processing Framework

https://github.com/yuchaoch/project/tree/main/Collaborative_Multi_BS_Power_Management_for_Dense_Radio_Access_Network_using_Deep_Reinforcement_Learning

Collaborative Multi-BS Power Management for Dense Radio Access Network using Deep Reinforcement Learning

https://github.com/yueqiw/gqn-world-model

World Models

https://github.com/yufeiwang63/L2D-learning-to-discretize

Learning to Discretize: Solving 1D Scalar Conservation Laws via Deep Reinforcement Learning

https://github.com/yuguangyue/Implicit_Distributional_RL

Implicit Distributional Reinforcement Learning

https://github.com/yuke93/Path-Restore

Path-Restore: Learning Network Path Selection for Image Restoration

https://github.com/yuke93/RL-Restore

Crafting a Toolchain for Image Restoration by Deep Reinforcement Learning

https://github.com/yukezhu/tensorflow-reinforce

Continuous control with deep reinforcement learning

https://github.com/yun-kwak/decision-transformer-jax

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/yusme/DDPG

Continuous control with deep reinforcement learning

https://github.com/yusuke0519/constrastive_predictive_coding

Representation Learning with Contrastive Predictive Coding

https://github.com/yusukeurakami/dreamer-pytorch

Dream to Control: Learning Behaviors by Latent Imagination

https://github.com/yusukeurakami/plan2explore-pytorch

Planning to Explore via Self-Supervised World Models

https://github.com/yusukeurakami/slac-playground

Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model

https://github.com/yuta0821/agent57_pytorch

Agent57: Outperforming the Atari Human Benchmark

https://github.com/ywchao/merel-mocap-gail

Learning human behaviors from motion capture by adversarial imitation

https://github.com/yytyvonne/DQN_agent_Chatbot

End-to-End Task-Completion Neural Dialogue Systems

https://github.com/yzhangee/NSCaching

NSCaching: Simple and Efficient Negative Sampling for Knowledge Graph Embedding

https://github.com/yzheng51/rl-dino-run

Deep Reinforcement Learning with Double Q-learning

https://github.com/zacharie12/zacharie

Learning to Compose Dynamic Tree Structures for Visual Contexts

https://github.com/zachary2940/ml-agents-assignment

Unity: A General Platform for Intelligent Agents

https://github.com/zacwellmer/worldmodels

World Models

https://github.com/zahinsufiyan/flow-networks-fault-adaptation

CFlowNets: Continuous Control with Generative Flow Networks

https://github.com/zanghyu/offline_bisimulation

MICo: Improved representations via sampling-based state similarity for Markov decision processes

https://github.com/zatang007/BayesianOptimization

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

https://github.com/zayedshah/Dumping-Green

Hybrid Code Networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning

https://github.com/zbyte64/pytorch-fuzzdom

Reinforcement Learning on Web Interfaces Using Workflow-Guided Exploration

https://github.com/zdhnarsil/stochastic-marginal-actor-critic

Dream to Control: Learning Behaviors by Latent Imagination

https://github.com/zeyuzhang1901/myltr

Unified Off-Policy Learning to Rank: a Reinforcement Learning Perspective

https://github.com/zhanghainan/RNN-S2S

Deep Reinforcement Learning for Dialogue Generation

https://github.com/zhanghainan/RNN-encdec

Deep Reinforcement Learning for Dialogue Generation

https://github.com/zhanghainan/ReinforcingCoherence

Deep Reinforcement Learning for Dialogue Generation

https://github.com/zhanghainan/TailoredSeq2Seq2DifferentConversationScenarios

Deep Reinforcement Learning for Dialogue Generation

https://github.com/zhanghainan/s2s-with-attention

Deep Reinforcement Learning for Dialogue Generation

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.