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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/robbycostales/diva

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Enabling Adaptive Agent Training in Open-Ended Simulators by Targeting Diversity

https://github.com/robbycostales/hal

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Possibility Before Utility: Learning And Using Hierarchical Affordances

https://github.com/robertmccarthy97/rrc_phase1

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Solving the Real Robot Challenge using Deep Reinforcement Learning

https://github.com/robfiras/ls-iq

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LS-IQ: Implicit Reward Regularization for Inverse Reinforcement Learning

https://github.com/robin-karlsson0/predictive-world-models

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Predictive World Models from Real-World Partial Observations

https://github.com/robin-schneider/gymCICY

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Explore and Exploit with Heterotic Line Bundle Models

https://github.com/robinhenry/gym-anm

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Gym-ANM: Reinforcement Learning Environments for Active Network Management Tasks in Electricity Distribution Systems

https://github.com/robinhenry/gym-anm-exp

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Gym-ANM: Reinforcement Learning Environments for Active Network Management Tasks in Electricity Distribution Systems

https://github.com/robintyh1/neurips2021-meta-gradient-offpolicy-evaluation

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Unifying Gradient Estimators for Meta-Reinforcement Learning via Off-Policy Evaluation

https://github.com/robinzixuan/IQN_Agent

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IGN : Implicit Generative Networks

https://github.com/robocin/rSoccer

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rSoccer: A Framework for Studying Reinforcement Learning in Small and Very Small Size Robot Soccer

https://github.com/roboterax/humanoid-gym

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Humanoid-Gym: Reinforcement Learning for Humanoid Robot with Zero-Shot Sim2Real Transfer

https://github.com/robotic-vision-lab/Next-Best-View-Via-Topological-Features

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Learning the Next Best View for 3D Point Clouds via Topological Features

https://github.com/robotology/gym-ignition

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Gym-Ignition: Reproducible Robotic Simulations for Reinforcement Learning

https://github.com/rodlaf/clusterenv

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High-Throughput Distributed Reinforcement Learning via Adaptive Policy Synchronization

https://github.com/rodrigocoelho7/vqc_qlearning

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VQC-Based Reinforcement Learning with Data Re-uploading: Performance and Trainability

https://github.com/roger-creus/surprise-adaptive-agents

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Surprise-Adaptive Intrinsic Motivation for Unsupervised Reinforcement Learning

https://github.com/rogerxcn/lunar_lander_project

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Solving The Lunar Lander Problem under Uncertainty using Reinforcement Learning

https://github.com/rohanpsingh/learninghumanoidwalking

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Learning Bipedal Walking On Planned Footsteps For Humanoid Robots

https://github.com/roman1e2f5p8s/rlapseingym

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Reinforcement Learning with Algorithms from Probabilistic Structure Estimation

https://github.com/romanbelaire/robust-ccer

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Regret-Based Defense in Adversarial Reinforcement Learning

https://github.com/rommeoijcai2019/rommeo

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A Regularized Opponent Model with Maximum Entropy Objective

https://github.com/ron-amit/Discount_as_Regularizer

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Discount Factor as a Regularizer in Reinforcement Learning

https://github.com/roosephu/boots

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On the Expressivity of Neural Networks for Deep Reinforcement Learning

https://github.com/roosephu/crabs

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Learning Barrier Certificates: Towards Safe Reinforcement Learning with Zero Training-time Violations

https://github.com/roosephu/slbo

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Algorithmic Framework for Model-based Deep Reinforcement Learning with Theoretical Guarantees

https://github.com/roozbehrazavi/bimrl

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BIMRL: Brain Inspired Meta Reinforcement Learning

https://github.com/rosewang2008/rmaddpg

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R-MADDPG for Partially Observable Environments and Limited Communication

https://github.com/rosieyzh/openrlhf-pretrain

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Echo Chamber: RL Post-training Amplifies Behaviors Learned in Pretraining

https://github.com/rotmanfinhub/gamma-vega-rl-hedging

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Gamma and Vega Hedging Using Deep Distributional Reinforcement Learning

https://github.com/rotskoff-group/marl-design

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Cooperative multi-agent reinforcement learning for high-dimensional nonequilibrium control

https://github.com/rowanlibr/sophisticated-learning

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Planning to Learn: A Novel Algorithm for Active Learning during Model-Based Planning

https://github.com/royalskye/agh

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Neural Airport Ground Handling

https://github.com/roythuly/obac

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Offline-Boosted Actor-Critic: Adaptively Blending Optimal Historical Behaviors in Deep Off-Policy RL

https://github.com/rparac/prob-irm

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Learning Robust Reward Machines from Noisy Labels

https://github.com/rpatrik96/AttA2C

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Attention-based Curiosity-driven Exploration in Deep Reinforcement Learning

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