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Reinforcement Learning

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코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/doerlbh/mentalRL

공식 코드

A Story of Two Streams: Reinforcement Learning Models from Human Behavior and Neuropsychiatry

https://github.com/dohyeoklee/SPQR

공식 코드

SPQR: Controlling Q-ensemble Independence with Spiked Random Model for Reinforcement Learning

https://github.com/donald-witt/fab-in-the-loop

공식 코드

Reinforcement Learning for Photonic Component Design

https://github.com/donamin/self-imitation-learning

공식 코드

Self-Imitation Learning of Locomotion Movements through Termination Curriculum

https://github.com/dongzhou-1996/sncoat

공식 코드

Space Non-cooperative Object Active Tracking with Deep Reinforcement Learning

https://github.com/dorlivne/PoPS

공식 코드

PoPS: Policy Pruning and Shrinking for Deep Reinforcement Learning

https://github.com/dornik/reagent

공식 코드

ReAgent: Point Cloud Registration using Imitation and Reinforcement Learning

https://github.com/dos-group/magpie

공식 코드

Magpie: Automatically Tuning Static Parameters for Distributed File Systems using Deep Reinforcement Learning

https://github.com/dppalomar/sparseindextracking

공식 코드

Reinforcement Learning for Financial Index Tracking

https://github.com/dracosource/draco

공식 코드

Multi-Agent Distributed Reinforcement Learning for Making Decentralized Offloading Decisions

https://github.com/drdh/synergy-rl

공식 코드

Low-Rank Modular Reinforcement Learning via Muscle Synergy

https://github.com/drl-casia/abcp

공식 코드

ABCP: Automatic Block-wise and Channel-wise Network Pruning via Joint Search

https://github.com/drl-casia/autonomous-driving

공식 코드

Lane Change Decision-Making through Deep Reinforcement Learning

https://github.com/drldeploymentchallenges-icsme2023/replicationpackage

공식 코드

Deploying Deep Reinforcement Learning Systems: A Taxonomy of Challenges

https://github.com/drlinter/drlinter

공식 코드

Faults in Deep Reinforcement Learning Programs: A Taxonomy and A Detection Approach

https://github.com/drmeerkat/Deep-Graph-Value-Network

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Towards Sample Efficient Agents through Algorithmic Alignment

https://github.com/drsleep/nas-segm-pytorch

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Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells

https://github.com/ds-jrg/causal-qa-rl

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Causal Question Answering with Reinforcement Learning

https://github.com/dsbrown1331/CoRL2019-DREX

공식 코드

Better-than-Demonstrator Imitation Learning via Automatically-Ranked Demonstrations

https://github.com/dsbrown1331/aaai-2018-code

공식 코드

Efficient Probabilistic Performance Bounds for Inverse Reinforcement Learning

https://github.com/dsbrown1331/broil

공식 코드

Bayesian Robust Optimization for Imitation Learning

https://github.com/dsbrown1331/machine-teaching-irl

공식 코드

Machine Teaching for Inverse Reinforcement Learning: Algorithms and Applications

https://github.com/dt6a/clorl

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Is Value Functions Estimation with Classification Plug-and-play for Offline Reinforcement Learning?

https://github.com/dt6a/rebrac

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Revisiting the Minimalist Approach to Offline Reinforcement Learning

https://github.com/dtabas/multiagent-particle-envs

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Interpreting Primal-Dual Algorithms for Constrained Multiagent Reinforcement Learning

https://github.com/dtak/POPCORN-POMDP

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POPCORN: Partially Observed Prediction COnstrained ReiNforcement Learning

https://github.com/dti-research/SenseActExperiments

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A Survey on Reproducibility by Evaluating Deep Reinforcement Learning Algorithms on Real-World Robots

https://github.com/dtroop/evt-bandits

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Risk-Averse Action Selection Using Extreme Value Theory Estimates of the CVaR

https://github.com/duggalrahul/CUP_Public

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CUP: Cluster Pruning for Compressing Deep Neural Networks

https://github.com/dunnolab/vintix

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Vintix: Action Model via In-Context Reinforcement Learning

https://github.com/dunnolab/xland-minigrid

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XLand-MiniGrid: Scalable Meta-Reinforcement Learning Environments in JAX

https://github.com/duojun-huang/alignsam-cvpr2024

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AlignSAM: Aligning Segment Anything Model to Open Context via Reinforcement Learning

https://github.com/duvenaud/relax

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Backpropagation through the Void: Optimizing control variates for black-box gradient estimation

https://github.com/dvlab-research/Seg-Zero

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VisionReasoner: Unified Visual Perception and Reasoning via Reinforcement Learning

https://github.com/dvlab-research/VisionReasoner

공식 코드

VisionReasoner: Unified Visual Perception and Reasoning via Reinforcement Learning

https://github.com/dvlab-research/arpo

공식 코드

ARPO:End-to-End Policy Optimization for GUI Agents with Experience Replay

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