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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/jrobine/actor-critic

Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation

https://github.com/jsanderink/tue

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

https://github.com/jsbaan/DPAC-DialogueGAN

Adversarial Learning for Neural Dialogue Generation

https://github.com/jsztompka/DuelDQN

Dueling Network Architectures for Deep Reinforcement Learning

https://github.com/jturner65/Getup-DartEnv

https://github.com/jugg1er/air

Value-Decomposition Networks For Cooperative Multi-Agent Learning

https://github.com/jukindle/aslaug3d_simulation

Whole-Body Control of a Mobile Manipulator using End-to-End Reinforcement Learning

https://github.com/juliakreutzer/bandit-neuralmonkey

Reliability and Learnability of Human Bandit Feedback for Sequence-to-Sequence Reinforcement Learning

https://github.com/juliakreutzer/joeynmt

An Actor-Critic Algorithm for Sequence Prediction

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

Dream to Control: Learning Behaviors by Latent Imagination

https://github.com/junming-yang/mopo

MOPO: Model-based Offline Policy Optimization

https://github.com/junmokane/offline-PEARL

Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables

https://github.com/junyoung0131/Mol-DQN

Optimization of Molecules via Deep Reinforcement Learning

https://github.com/justinjfu/diagnosing_qlearning

Diagnosing Bottlenecks in Deep Q-learning Algorithms

https://github.com/justinjfu/inverse_rl

A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models

https://github.com/justinmaojones/starr

Prioritized Experience Replay

https://github.com/jvidals09/Decentralized-and-multi-agent-control-of-Franka-Emika-Panda-robot-in-continuous-task-execution

Soft Actor-Critic Algorithms and Applications

https://github.com/jviquerat/pbo

Single-step deep reinforcement learning for open-loop control of laminar and turbulent flows

https://github.com/jvking/text-games

Deep Reinforcement Learning with a Natural Language Action Space

https://github.com/jvoynow/DQN-analysis-with-2048

Deep Reinforcement Learning with Double Q-learning

https://github.com/jxu43/replication-mbpo

When to Trust Your Model: Model-Based Policy Optimization

https://github.com/jxwuyi/House3D

Embodied Question Answering

https://github.com/jyqhahah/rl_maddpg_matd3

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

https://github.com/k-lombard/CS4641_Project

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

https://github.com/k-lombard/Deep-Learning-Chess-AI

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

https://github.com/k-r-allen/residual-policy-learning

Residual Policy Learning

https://github.com/kachayev/car-racing-attention-agent

Neuroevolution of Self-Interpretable Agents

https://github.com/kachayev/gym-microrts-paper-sb3

Gym-$μ$RTS: Toward Affordable Full Game Real-time Strategy Games Research with Deep Reinforcement Learning

https://github.com/kairproject/kair_algorithms_draft

Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards

https://github.com/kaist-silab/symmetric_replay

POMO: Policy Optimization with Multiple Optima for Reinforcement Learning

https://github.com/kaixindelele/DRLib

Overcoming Exploration in Reinforcement Learning with Demonstrations

https://github.com/kalebbennaveed/Trajectory-Planning-for-Autonomous-Vehicles-Using-HRL

Trajectory Planning for Autonomous Vehicles Using Hierarchical Reinforcement Learning

https://github.com/kaletap/deep-cube-rl

Solving the Rubik's Cube Without Human Knowledge

https://github.com/kalifou/robotics-rl-srl

Simple random search provides a competitive approach to reinforcement learning

https://github.com/kangkang59812/GraphCaption

Self-critical Sequence Training for Image Captioning

https://github.com/kantneel/causal-metarl

Causal Reasoning from Meta-reinforcement Learning

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