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Reinforcement Learning (RL)

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보존된 평가 기록: 3

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/osilly/vision-r1

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Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language Models

https://github.com/osrlpaper/os_rl_papercode

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Deep Reinforcement Learning for Optimal Stopping with Application in Financial Engineering

https://github.com/othmaneM/RL_adap_stepsize

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Improving reinforcement learning algorithms: towards optimal learning rate policies

https://github.com/otmhi/offpolicy_ls

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Logarithmic Smoothing for Pessimistic Off-Policy Evaluation, Selection and Learning

https://github.com/ouazusakou/imagination_mechanism

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Enhancing data efficiency in reinforcement learning: a novel imagination mechanism based on mesh information propagation

https://github.com/owenonline/knowledge-graph-reasoning-with-self-supervised-reinforcement-learning

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Knowledge Graph Reasoning with Self-supervised Reinforcement Learning

https://github.com/oxwhirl/loaded-dice

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Loaded DiCE: Trading off Bias and Variance in Any-Order Score Function Estimators for Reinforcement Learning

https://github.com/oxwhirl/opiq

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Optimistic Exploration even with a Pessimistic Initialisation

https://github.com/oxwhirl/pymarl

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QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

https://github.com/oxwhirl/smac

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The StarCraft Multi-Agent Challenge

https://github.com/oxwhirl/treeqn

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TreeQN and ATreeC: Differentiable Tree-Structured Models for Deep Reinforcement Learning

https://github.com/oxwhirl/wqmix

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Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

https://github.com/p-christ/Deep-Reinforcement-Learning-Algorithms-with-PyTorch

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Soft Actor-Critic for Discrete Action Settings

https://github.com/p3jitnath/climate-rl

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RAIN: Reinforcement Algorithms for Improving Numerical Weather and Climate Models

https://github.com/pablovin/ChefsHatGYM

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Learning from Learners: Adapting Reinforcement Learning Agents to be Competitive in a Card Game

https://github.com/pablovin/ChefsHatPlayersClub

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Incorporating Rivalry in Reinforcement Learning for a Competitive Game

https://github.com/padlex/reinforcement-learning-from-explicitly-programmed-reward-signals

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Exploring RL-based LLM Training for Formal Language Tasks with Programmed Rewards

https://github.com/pairlab/d2rl

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D2RL: Deep Dense Architectures in Reinforcement Learning

https://github.com/pairlab/vagram

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Value Gradient weighted Model-Based Reinforcement Learning

https://github.com/pangolulu/exact-k-recommendation

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Exact-K Recommendation via Maximal Clique Optimization

https://github.com/panmt/vid2act

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Model-Based Reinforcement Learning with Multi-Task Offline Pretraining

https://github.com/panshark/pirl-win

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Digital Twin-Enhanced Wireless Indoor Navigation: Achieving Efficient Environment Sensing with Zero-Shot Reinforcement Learning

https://github.com/panxulab/distributionally-robust-lsvi-ucb

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Distributionally Robust Off-Dynamics Reinforcement Learning: Provable Efficiency with Linear Function Approximation

https://github.com/panxulab/lsvi-ase

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More Efficient Randomized Exploration for Reinforcement Learning via Approximate Sampling

https://github.com/panyxy/hlgp_cvrp

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Hierarchical Learning-based Graph Partition for Large-scale Vehicle Routing Problems

https://github.com/paoloae/paper_rl_blackbox_thermal_machines

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Model-free optimization of power/efficiency tradeoffs in quantum thermal machines using reinforcement learning

https://github.com/paoloae/paper_rl_thermal_machines

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Identifying optimal cycles in quantum thermal machines with reinforcement-learning

https://github.com/paperflight/Fed-MF-MAL

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Scalable Multi-agent Reinforcement Learning Algorithm for Wireless Networks

https://github.com/paperflight/Hierarchical-Multi-agent-DRL-with-Federated-Learning

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Correlation-aware Cooperative Multigroup Broadcast 360° Video Delivery Network: A Hierarchical Deep Reinforcement Learning Approach

https://github.com/paranoidandroid96/task-phasing

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Task Phasing: Automated Curriculum Learning from Demonstrations

https://github.com/parnika31/MACAAC

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Attention Actor-Critic algorithm for Multi-Agent Constrained Co-operative Reinforcement Learning

https://github.com/parnika31/MACAAC_Supplementary

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Attention Actor-Critic algorithm for Multi-Agent Constrained Co-operative Reinforcement Learning

https://github.com/parshakova/GAMS-for-Data-Efficient-Learning

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Distributional Reinforcement Learning for Energy-Based Sequential Models

https://github.com/pascalson/kgrl

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Flexible Attention-Based Multi-Policy Fusion for Efficient Deep Reinforcement Learning

https://github.com/pat-jj/deepretrieval

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DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning

https://github.com/pat-jj/s3

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s3: You Don't Need That Much Data to Train a Search Agent via RL

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