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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/baumanndominik/ergodic_rl

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Reinforcement learning with non-ergodic reward increments: robustness via ergodicity transformations

https://github.com/bbrighttaer/irelease

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Deep Inverse Reinforcement Learning for Structural Evolution of Small Molecules

https://github.com/bcornelusse/microgridRLsimulator

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Lifelong Control of Off-grid Microgrid with Model Based Reinforcement Learning

https://github.com/beanie00/lesson

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LESSON: Learning to Integrate Exploration Strategies for Reinforcement Learning via an Option Framework

https://github.com/bee-hive/kdbirl_public

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Kernel Density Bayesian Inverse Reinforcement Learning

https://github.com/befelix/safe-exploration

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Learning-based Model Predictive Control for Safe Exploration and Reinforcement Learning

https://github.com/befelix/safe_learning

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Safe Model-based Reinforcement Learning with Stability Guarantees

https://github.com/behavioral-data/PARTNER

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Towards Facilitating Empathic Conversations in Online Mental Health Support: A Reinforcement Learning Approach

https://github.com/behaviorguidedRL/BGRL

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Learning to Score Behaviors for Guided Policy Optimization

https://github.com/behzadanksu/rl-attack

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Whatever Does Not Kill Deep Reinforcement Learning, Makes It Stronger

https://github.com/belinghy/SteppingStone

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ALLSTEPS: Curriculum-driven Learning of Stepping Stone Skills

https://github.com/bellmantimehut/dipo

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Policy Representation via Diffusion Probability Model for Reinforcement Learning

https://github.com/ben-eysenbach/info_geometry

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The Information Geometry of Unsupervised Reinforcement Learning

https://github.com/ben-eysenbach/mnm

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Mismatched No More: Joint Model-Policy Optimization for Model-Based RL

https://github.com/benedekrozemberczki/shapley

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The Shapley Value in Machine Learning

https://github.com/bengioe/condnet

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Conditional Computation in Neural Networks for faster models

https://github.com/bengioe/staleness-corrected-momentum

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Correcting Momentum in Temporal Difference Learning

https://github.com/benlansdell/synthfeedback

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Learning to solve the credit assignment problem

https://github.com/berkeleyautomation/rlqp

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Accelerating Quadratic Optimization with Reinforcement Learning

https://github.com/betray12138/task-representation-shift

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Scrutinize What We Ignore: Reining In Task Representation Shift Of Context-Based Offline Meta Reinforcement Learning

https://github.com/betsyhj/rl4rec

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State Encoders in Reinforcement Learning for Recommendation: A Reproducibility Study

https://github.com/bgasperov/drlformm

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Deep Reinforcement Learning for Market Making Under a Hawkes Process-Based Limit Order Book Model

https://github.com/big-s2/causal-deepsets

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Causal Deepsets for Off-policy Evaluation under Spatial or Spatio-temporal Interferences

https://github.com/bigai-ai/ToM-RL

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Do Theory of Mind Benchmarks Need Explicit Human-like Reasoning in Language Models?

https://github.com/bigai-nlco/rulereasoner

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RuleReasoner: Reinforced Rule-based Reasoning via Domain-aware Dynamic Sampling

https://github.com/bigrl-team/gear

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GEAR: A GPU-Centric Experience Replay System for Large Reinforcement Learning Models

https://github.com/billchan226/poar-srl-4-robot

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POAR: Efficient Policy Optimization via Online Abstract State Representation Learning

https://github.com/billhhh/krpo_llms_rl

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Kalman Filter Enhanced GRPO for Reinforcement Learning-Based Language Model Reasoning

https://github.com/binary-husky/hmp2g

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A Cooperation Graph Approach for Multiagent Sparse Reward Reinforcement Learning

https://github.com/binary-husky/unreal-hmp

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Learning Heterogeneous Agent Cooperation via Multiagent League Training

https://github.com/birlrobotics/rlkit_autotune

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Hyperparameter Auto-tuning in Self-Supervised Robotic Learning

https://github.com/bit-xjy/rl-based-transferable-ems

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A Comparative Study of Deep Reinforcement Learning-based Transferable Energy Management Strategies for Hybrid Electric Vehicles

https://github.com/bit1029public/ADRQN

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On Improving Deep Reinforcement Learning for POMDPs

https://github.com/bit1029public/offline_bpr

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Behavior Prior Representation learning for Offline Reinforcement Learning

https://github.com/bit1029public/simsr

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SimSR: Simple Distance-based State Representation for Deep Reinforcement Learning

https://github.com/bjliaa/c2d

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Conjugated Discrete Distributions for Distributional Reinforcement Learning

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