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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/yzhao3685/stoccontextualopti

Balanced Off-Policy Evaluation for Personalized Pricing

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

Deep Reinforcement Learning with Double Q-learning

https://github.com/zacwellmer/worldmodels

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/zexusun/oilca-neurips23

Discriminator-Weighted Offline Imitation Learning from Suboptimal Demonstrations

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

https://github.com/zhaoolee/garss

Offline Reinforcement Learning for LLM Multi-Step Reasoning

https://github.com/zhaoyi11/tcrl

Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning

https://github.com/zhengant/dqn_reversi

Deep Reinforcement Learning with Double Q-learning

https://github.com/zhengsr3/Reinforcement_Learning_Pointer_Networks_TSP_Pytorch

Neural Combinatorial Optimization with Reinforcement Learning

https://github.com/zhixuan-lin/dreamer-pytorch

Dream to Control: Learning Behaviors by Latent Imagination

https://github.com/zhixuan-lin/ensemble-rl-discrete

Dopamine: A Research Framework for Deep Reinforcement Learning

https://github.com/zhou-henry/distributed-distributional-drq

Distributed Distributional Deterministic Policy Gradients

https://github.com/zhoujianxing123/minigo_v17

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

https://github.com/zhyack/UDRG

A Semi-Supervised Approach for Low-Resourced Text Generation

https://github.com/ziangqin-stu/impl_data-effiient-hrl

Data-Efficient Hierarchical Reinforcement Learning

https://github.com/ziangqin-stu/impl_hiro

Data-Efficient Hierarchical Reinforcement Learning

https://github.com/ziangqin-stu/rl_hiro

Data-Efficient Hierarchical Reinforcement Learning

https://github.com/zifanwu/cal

Constrained Variational Policy Optimization for Safe Reinforcement Learning

https://github.com/zihanwang314/ragen

RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning

https://github.com/zijianh4/CROP-leaderboard.github.io

CROP: Certifying Robust Policies for Reinforcement Learning through Functional Smoothing

https://github.com/zilongzheng/visdial-gnn

Learning Cooperative Visual Dialog Agents with Deep Reinforcement Learning

https://github.com/zilunpeng/svrg_for_policy_evaluation_with_fewer_gradients

SVRG for Policy Evaluation with Fewer Gradient Evaluations

https://github.com/zivaharoni/capacity-rl

Computing the Feedback Capacity of Finite State Channels using Reinforcement Learning

https://github.com/ziyizhang27/sdpo

REBEL: Reinforcement Learning via Regressing Relative Rewards

https://github.com/zjunet/amnet

Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters

https://github.com/zoeyuchao/MPE-pytorch

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

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