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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/melkael/vne

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Monkey Business: Reinforcement learning meets neighborhood search for Virtual Network Embedding

https://github.com/mengdi-li/internally-rewarded-rl

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Internally Rewarded Reinforcement Learning

https://github.com/mengf1/PAL

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Learning how to Active Learn: A Deep Reinforcement Learning Approach

https://github.com/menggedu/discover

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DISCOVER: Deep identification of symbolically concise open-form PDEs via enhanced reinforcement-learning

https://github.com/mengli11235/swin-dqn

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Deep Reinforcement Learning with Swin Transformers

https://github.com/metadriverse/metadrive

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MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement Learning

https://github.com/mewtwo1996/relax

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ReLAX: Reinforcement Learning Agent eXplainer for Arbitrary Predictive Models

https://github.com/mfe7/cadrl_ros

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Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning

https://github.com/mfigura/Resilient-consensus-based-MARL

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Resilient Consensus-based Multi-agent Reinforcement Learning with Function Approximation

https://github.com/mfocchi/jump_rl

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Efficient Reinforcement Learning for Jumping Monopods

https://github.com/mgbukov/rl_disentangle

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Reinforcement Learning to Disentangle Multiqubit Quantum States from Partial Observations

https://github.com/mgoulao/tov-vicreg

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Pretraining the Vision Transformer using self-supervised methods for vision based Deep Reinforcement Learning

https://github.com/mhammoud115/da-rl

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Data Assimilation in Chaotic Systems Using Deep Reinforcement Learning

https://github.com/mhauskn/dqn-hfo

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Deep Reinforcement Learning in Parameterized Action Space

https://github.com/mhngu23/Variable-Agnostic-Causal-Exploration-for-Reinforcement-Learning-VACERL

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Variable-Agnostic Causal Exploration for Reinforcement Learning

https://github.com/micdoh/XLRON

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Reinforcement Learning for Dynamic Resource Allocation in Optical Networks: Hype or Hope?

https://github.com/michael-beukman/robocupgym

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RobocupGym: A challenging continuous control benchmark in Robocup

https://github.com/michaelliyunhao/skeleton2humanoid

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Skeleton2Humanoid: Animating Simulated Characters for Physically-plausible Motion In-betweening

https://github.com/michaeltmatthews/craftax

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Craftax: A Lightning-Fast Benchmark for Open-Ended Reinforcement Learning

https://github.com/michaeltmatthews/jax2d

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Kinetix: Investigating the Training of General Agents through Open-Ended Physics-Based Control Tasks

https://github.com/michaelwan11/hfr

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Hindsight Foresight Relabeling for Meta-Reinforcement Learning

https://github.com/michalbortkiewicz/jaxgcrl

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Accelerating Goal-Conditioned RL Algorithms and Research

https://github.com/microndla/rl_streaming_engine

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Reinforcement Learning Approach for Mapping Applications to Dataflow-Based Coarse-Grained Reconfigurable Array

https://github.com/microsoft/IBAC-SNI

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Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck

https://github.com/microsoft/Mask-based-Latent-Reconstruction

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Mask-based Latent Reconstruction for Reinforcement Learning

https://github.com/microsoft/Playvirtual

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PlayVirtual: Augmenting Cycle-Consistent Virtual Trajectories for Reinforcement Learning

https://github.com/microsoft/atac

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Adversarially Trained Actor Critic for Offline Reinforcement Learning

https://github.com/microsoft/logrl

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Using a Logarithmic Mapping to Enable Lower Discount Factors in Reinforcement Learning

https://github.com/microsoft/orchestrated-value-mapping

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Orchestrated Value Mapping for Reinforcement Learning

https://github.com/microsoft/rl-offline-simulation

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Towards Data-Driven Offline Simulations for Online Reinforcement Learning

https://github.com/microsoft/strategically_efficient_rl

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Strategically Efficient Exploration in Competitive Multi-agent Reinforcement Learning

https://github.com/miguel-kjh/Improving-Dialogue-Management

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Improving Dialogue Management: Quality Datasets vs Models

https://github.com/miguelsvasco/gmc

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Geometric Multimodal Contrastive Representation Learning

https://github.com/miguelsvasco/muse

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How to Sense the World: Leveraging Hierarchy in Multimodal Perception for Robust Reinforcement Learning Agents

https://github.com/mikata-project/adcraft

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AdCraft: An Advanced Reinforcement Learning Benchmark Environment for Search Engine Marketing Optimization

https://github.com/mike-gimelfarb/bayesian-epsilon-greedy

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ε-BMC: A Bayesian Ensemble Approach to Epsilon-Greedy Exploration in Model-Free Reinforcement Learning

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