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Reinforcement Learning (RL)

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하위 태스크: 6
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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/lizzyhku/oasd

공식 코드

Online Anomalous Subtrajectory Detection on Road Networks with Deep Reinforcement Learning

https://github.com/lknak/tigr

공식 코드

Meta-Reinforcement Learning in Broad and Non-Parametric Environments

https://github.com/llan-ml/tesp

공식 코드

Meta Reinforcement Learning with Task Embedding and Shared Policy

https://github.com/lll6gg/ui-r1

공식 코드

UI-R1: Enhancing Efficient Action Prediction of GUI Agents by Reinforcement Learning

https://github.com/llmpfc/icml24sub

공식 코드

Improving Planning with Large Language Models: A Modular Agentic Architecture

https://github.com/lmb-freiburg/td-or-not-td

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TD or not TD: Analyzing the Role of Temporal Differencing in Deep Reinforcement Learning

https://github.com/lmzintgraf/cavia

공식 코드

Fast Context Adaptation via Meta-Learning

https://github.com/lmzintgraf/hyperx

공식 코드

Exploration in Approximate Hyper-State Space for Meta Reinforcement Learning

https://github.com/lockwo/rl_qvc_opt

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Optimizing Quantum Variational Circuits with Deep Reinforcement Learning

https://github.com/locuslab/ase

공식 코드

Provably Safe PAC-MDP Exploration Using Analogies

https://github.com/locuslab/icnn

공식 코드

Input Convex Neural Networks

https://github.com/loganriggs/optimal-policies-tend-to-seek-power

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Optimal Policies Tend to Seek Power

https://github.com/logue311/EDNAS

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Efficient Decoupled Neural Architecture Search by Structure and Operation Sampling

https://github.com/lollcat/DistillationTrain-Gym

공식 코드

Deep Reinforcement Learning for Process Synthesis

https://github.com/lollcat/aspen-rl

공식 코드

Synthesis of separation processes with reinforcement learning

https://github.com/londonnode/pearl

공식 코드

Pearl: Parallel Evolutionary and Reinforcement Learning Library

https://github.com/longkangli/pfss-il

공식 코드

Learning to Optimize Permutation Flow Shop Scheduling via Graph-based Imitation Learning

https://github.com/lostxine/elo-rainbow

공식 코드

Does Self-supervised Learning Really Improve Reinforcement Learning from Pixels?

https://github.com/lpphd/CHARLEE

공식 코드

Multivariate Time Series Early Classification Across Channel and Time Dimensions

https://github.com/lqnew/sdq-cal

공식 코드

Simultaneous Double Q-learning with Conservative Advantage Learning for Actor-Critic Methods

https://github.com/lrhammond/almanac

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Multi-Agent Reinforcement Learning with Temporal Logic Specifications

https://github.com/lrhammond/lmorl

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Lexicographic Multi-Objective Reinforcement Learning

https://github.com/ltl3a87/compa

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Unveiling the Compositional Ability Gap in Vision-Language Reasoning Model

https://github.com/lucaballotta/processingnetworks-rl

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A Reinforcement Learning Approach to Sensing Design in Resource-Constrained Wireless Networked Control Systems

https://github.com/lucasalegre/morl-baselines

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Sample-Efficient Multi-Objective Learning via Generalized Policy Improvement Prioritization

https://github.com/lucasalegre/sfols

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Optimistic Linear Support and Successor Features as a Basis for Optimal Policy Transfer

https://github.com/lucascjysdl/deepfreight

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DeepFreight: Integrating Deep Reinforcement Learning and Mixed Integer Programming for Multi-transfer Truck Freight Delivery

https://github.com/lucascjysdl/hierairl

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Option-Aware Adversarial Inverse Reinforcement Learning for Robotic Control

https://github.com/lucascjysdl/offline-rl-kit

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Bayes Adaptive Monte Carlo Tree Search for Offline Model-based Reinforcement Learning

https://github.com/lucidrains/perceiver-pytorch

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Decentralized Transformers with Centralized Aggregation are Sample-Efficient Multi-Agent World Models

https://github.com/luciferkonn/moc_corl22

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Learning Multi-Objective Curricula for Robotic Policy Learning

https://github.com/luckeciano/transformers-metarl

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Transformers are Meta-Reinforcement Learners

https://github.com/lueckem/quadrature-ML

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Efficient time stepping for numerical integration using reinforcement learning

https://github.com/lukaswill/causal-xrl

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Causal State Distillation for Explainable Reinforcement Learning

https://github.com/luksfarris/pydeeprecsys

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Optimized Recommender Systems with Deep Reinforcement Learning

https://github.com/lukun199/semanticrl

공식 코드

Reinforcement Learning-powered Semantic Communication via Semantic Similarity

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