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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/albertipot/nar

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Generalized Global Ranking-Aware Neural Architecture Ranker for Efficient Image Classifier Search

https://github.com/albertometelli/pfqi

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Control Frequency Adaptation via Action Persistence in Batch Reinforcement Learning

https://github.com/albicaron/counterdkl

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Counterfactual Learning with Multioutput Deep Kernels

https://github.com/aleksispi/airloc

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Aerial View Localization with Reinforcement Learning: Towards Emulating Search-and-Rescue

https://github.com/aleksispi/pose-drl

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Deep Reinforcement Learning for Active Human Pose Estimation

https://github.com/alevine0/reengage

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Goal-Conditioned Q-Learning as Knowledge Distillation

https://github.com/alex-petrenko/megaverse

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Megaverse: Simulating Embodied Agents at One Million Experiences per Second

https://github.com/alex-petrenko/sample-factory

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Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement Learning

https://github.com/alex-yanranwang/consdred-smpc

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Constrained Reinforcement Learning using Distributional Representation for Trustworthy Quadrotor UAV Tracking Control

https://github.com/alexfrom0815/Online-3D-BPP-DRL

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Online 3D Bin Packing with Constrained Deep Reinforcement Learning

https://github.com/alexfrom0815/ir-bpp

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Learning Physically Realizable Skills for Online Packing of General 3D Shapes

https://github.com/alexgoldie/rl-learned-optimization

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Can Learned Optimization Make Reinforcement Learning Less Difficult?

https://github.com/alexlee-gk/citysim3d

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Learning Visual Servoing with Deep Features and Fitted Q-Iteration

https://github.com/alexlee-gk/visual_dynamics

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Learning Visual Servoing with Deep Features and Fitted Q-Iteration

https://github.com/alexlioralexli/learned-fourier-features

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Functional Regularization for Reinforcement Learning via Learned Fourier Features

https://github.com/alexsax/midlevel-reps

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Learning to Navigate Using Mid-Level Visual Priors

https://github.com/alfredvc/paac

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Efficient Parallel Methods for Deep Reinforcement Learning

https://github.com/alga-hopf/drl-graph-partitioning

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Graph Partitioning and Sparse Matrix Ordering using Reinforcement Learning and Graph Neural Networks

https://github.com/algorithmic-alignment-lab/contracts

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Formal Contracts Mitigate Social Dilemmas in Multi-Agent RL

https://github.com/alherit/cr-dqn

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A Cramér Distance perspective on Quantile Regression based Distributional Reinforcement Learning

https://github.com/alibaba-nlp/masksearch

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MASKSEARCH: A Universal Pre-Training Framework to Enhance Agentic Search Capability

https://github.com/alibaba-nlp/zerosearch

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ZeroSearch: Incentivize the Search Capability of LLMs without Searching

https://github.com/alibeikmohammadi/opennars-for-applications

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NARS vs. Reinforcement learning: ONA vs. Q-Learning

https://github.com/alignmentresearch/vlmrm

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Vision-Language Models are Zero-Shot Reward Models for Reinforcement Learning

https://github.com/allenai/ai2thor

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AI2-THOR: An Interactive 3D Environment for Visual AI

https://github.com/allenai/promptable-behaviors

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Promptable Behaviors: Personalizing Multi-Objective Rewards from Human Preferences

https://github.com/allenai/rl4lms

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Is Reinforcement Learning (Not) for Natural Language Processing: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimization

https://github.com/alouetteuio/hotspot_nmi

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Using reinforcement learning to improve drone-based inference of greenhouse gas fluxes

https://github.com/alpaficia/MOSEAC

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MOSEAC: Streamlined Variable Time Step Reinforcement Learning

https://github.com/alpaficia/SEAC_Pytorch_release

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Deployable Reinforcement Learning with Variable Control Rate

https://github.com/alpc91/sgrl

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Subequivariant Graph Reinforcement Learning in 3D Environments

https://github.com/alstn12088/adaptive-teacher

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Adaptive teachers for amortized samplers

https://github.com/amal994/experiential-explanations-rl

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Experiential Explanations for Reinforcement Learning

https://github.com/amap-ml/gpg

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GPG: A Simple and Strong Reinforcement Learning Baseline for Model Reasoning

https://github.com/amartyamukherjee/ReinforcementLearningCartPosition

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A Comparison of Reward Functions in Q-Learning Applied to a Cart Position Problem

https://github.com/amathislab/kinesis

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Reinforcement learning-based motion imitation for physiologically plausible musculoskeletal motor control

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