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Reinforcement Learning (RL)

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하위 태스크: 6
평가표: 2
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/fmorenovr/DRDQ-Atari

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Performing Deep Recurrent Double Q-Learning for Atari Games

https://github.com/fmpr/CAREL

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Towards Robust Deep Reinforcement Learning for Traffic Signal Control: Demand Surges, Incidents and Sensor Failures

https://github.com/fomorians/gym_tool_use

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Reasoning and Generalization in RL: A Tool Use Perspective

https://github.com/fortsandmills/rl-theory-book

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Reinforcement Learning Textbook

https://github.com/forzaeth/rlpp

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RLPP: A Residual Method for Zero-Shot Real-World Autonomous Racing on Scaled Platforms

https://github.com/frankg1/SL-MGAC-torch

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Supervised Learning-enhanced Multi-Group Actor Critic for Live Stream Allocation in Feed

https://github.com/frankroeder/lanro-gym

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Language-Conditioned Reinforcement Learning to Solve Misunderstandings with Action Corrections

https://github.com/frankzheng2022/taco

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TACO: Temporal Latent Action-Driven Contrastive Loss for Visual Reinforcement Learning

https://github.com/franroldans/qmp-her

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Learning and reusing primitive behaviours to improve Hindsight Experience Replay sample efficiency

https://github.com/fraunhoferiee/curriculumagent

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Managing power grids through topology actions: A comparative study between advanced rule-based and reinforcement learning agents

https://github.com/fredericometelo/peersim-environment

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PeersimGym: An Environment for Solving the Task Offloading Problem with Reinforcement Learning

https://github.com/fredericometelo/taskoffloadingagentlibrary

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PeersimGym: An Environment for Solving the Task Offloading Problem with Reinforcement Learning

https://github.com/freedomintelligence/huatuogpt-o1

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HuatuoGPT-o1, Towards Medical Complex Reasoning with LLMs

https://github.com/frenkowski/scimai-gym

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Comparing Deep Reinforcement Learning Algorithms in Two-Echelon Supply Chains

https://github.com/friedrichgeiecke/dynamic_treatment

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Dynamically Optimal Treatment Allocation

https://github.com/frietz58/psqd

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Prioritized Soft Q-Decomposition for Lexicographic Reinforcement Learning

https://github.com/frt03/generalized_dt

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Generalized Decision Transformer for Offline Hindsight Information Matching

https://github.com/frt03/inference-based-rl

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Co-Adaptation of Algorithmic and Implementational Innovations in Inference-based Deep Reinforcement Learning

https://github.com/frt03/pic

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Policy Information Capacity: Information-Theoretic Measure for Task Complexity in Deep Reinforcement Learning

https://github.com/fslight1996/SHER

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Soft Hindsight Experience Replay

https://github.com/fsmiu/Two-way-Deconfounder

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Two-way Deconfounder for Off-policy Evaluation in Causal Reinforcement Learning

https://github.com/fstrnad/pyDRLinWESM

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Deep reinforcement learning in World-Earth system models to discover sustainable management strategies

https://github.com/fu-feng/grl

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Genes in Intelligent Agents

https://github.com/fudandisc/discopen-medbox-dialodiagnosis

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Hierarchical Reinforcement Learning for Automatic Disease Diagnosis

https://github.com/fujitsuresearch/safeexploration

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Safe Exploration Method for Reinforcement Learning under Existence of Disturbance

https://github.com/fuqichen1998/SequentialVotingDet

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Sequential Voting with Relational Box Fields for Active Object Detection

https://github.com/fusion-ml/trajectory-information-rl

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Exploration via Planning for Information about the Optimal Trajectory

https://github.com/fuxiailab/rl4rs

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RL4RS: A Real-World Dataset for Reinforcement Learning based Recommender System

https://github.com/fuxiailab/xrl-bench

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XRL-Bench: A Benchmark for Evaluating and Comparing Explainable Reinforcement Learning Techniques

https://github.com/fuyw/furl

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FuRL: Visual-Language Models as Fuzzy Rewards for Reinforcement Learning

https://github.com/fzp0424/mt-r1-zero

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MT-R1-Zero: Advancing LLM-based Machine Translation via R1-Zero-like Reinforcement Learning

https://github.com/g0k0ururi/crop

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CROP: Conservative Reward for Model-based Offline Policy Optimization

https://github.com/gablg1/ORGAN

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Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models

https://github.com/gabrieledcjr/DeepRL

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Pre-training with Non-expert Human Demonstration for Deep Reinforcement Learning

https://github.com/gabrieletiboni/dropo

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DROPO: Sim-to-Real Transfer with Offline Domain Randomization

https://github.com/gaetanserre/l2rpn-2022_ppo-baseline

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Reinforcement learning for Energies of the future and carbon neutrality: a Challenge Design

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