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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/intelligent-control-lab/physicsawarecombinatorialasp

공식 코드

Physics-Aware Combinatorial Assembly Sequence Planning using Data-free Action Masking

https://github.com/intelligent-control-lab/statewise_constrained_policy_optimization

공식 코드

State-wise Constrained Policy Optimization

https://github.com/intelligent-reliable-autonomous-systems/gcrs-expert-abstractions

공식 코드

Learning with Expert Abstractions for Efficient Multi-Task Continuous Control

https://github.com/inventec-ai-center/carl-siggraph2020

공식 코드

CARL: Controllable Agent with Reinforcement Learning for Quadruped Locomotion

https://github.com/iosband/ts_tutorial

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A Tutorial on Thompson Sampling

https://github.com/irmvlab/rlsac

공식 코드

RLSAC: Reinforcement Learning enhanced Sample Consensus for End-to-End Robust Estimation

https://github.com/irom-lab/Invariant-Policy-Optimization

공식 코드

Invariant Policy Optimization: Towards Stronger Generalization in Reinforcement Learning

https://github.com/irom-lab/PAC-Vision-Planning

공식 코드

Probably Approximately Correct Vision-Based Planning using Motion Primitives

https://github.com/is0383kk/symbolemergence-vae-gmm

공식 코드

Emergent Communication through Metropolis-Hastings Naming Game with Deep Generative Models

https://github.com/isayev/ReLeaSE

공식 코드

Deep Reinforcement Learning for De-Novo Drug Design

https://github.com/isend-clemson/drl-bems

공식 코드

Energy Optimization for HVAC Systems in Multi-VAV Open Offices: A Deep Reinforcement Learning Approach

https://github.com/ishohei220/adopt

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ADOPT: Modified Adam Can Converge with Any $β_2$ with the Optimal Rate

https://github.com/ispc-lab/marl4ad

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Multi-Agent Reinforcement Learning for Autonomous Driving: A Survey

https://github.com/ita9naiwa/rlrd-aaai2022

공식 코드

An Efficient Combinatorial Optimization Model Using Learning-to-Rank Distillation

https://github.com/itstyren/InteractionMARL-Coop

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Enhancing Cooperation through Selective Interaction and Long-term Experiences in Multi-Agent Reinforcement Learning

https://github.com/iurteaga/bandits

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Bayesian bandits: balancing the exploration-exploitation tradeoff via double sampling

https://github.com/jacarvalho/nopg

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A Nonparametric Off-Policy Policy Gradient

https://github.com/jackblandin/fairness-comparison

공식 코드

Fairness Through Counterfactual Utilities

https://github.com/jackieli19/pgportfolio

공식 코드

A Deep Reinforcement Learning Framework For Financial Portfolio Management

https://github.com/jacklinkk/torchgrl

공식 코드

Graph Convolution-Based Deep Reinforcement Learning for Multi-Agent Decision-Making in Mixed Traffic Environments

https://github.com/jackory/rpbt

공식 코드

Learning Diverse Risk Preferences in Population-based Self-play

https://github.com/jacksonchen1998/Empowering-NLG

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Empowering NLG: Offline Reinforcement Learning for Informal Summarization in Online Domains

https://github.com/jackyk02/distributed_rl_benchmarks

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Efficient Parallel Reinforcement Learning Framework using the Reactor Model

https://github.com/jacobandreas/l3

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Learning with Latent Language

https://github.com/jacobandreas/nmn2

공식 코드

Learning to Compose Neural Networks for Question Answering

https://github.com/jacobandreas/psketch

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Modular Multitask Reinforcement Learning with Policy Sketches

https://github.com/jacobfulano/learning-rules-with-bmi

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Distinguishing Learning Rules with Brain Machine Interfaces

https://github.com/jacobha/q-bounding-in-compositional-rl

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Bounding the Optimal Value Function in Compositional Reinforcement Learning

https://github.com/jacobkooi/hadamax

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Hadamax Encoding: Elevating Performance in Model-Free Atari

https://github.com/jacooba/hyper

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Hypernetworks in Meta-Reinforcement Learning

https://github.com/jacopod/udrlpg_

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Upside Down Reinforcement Learning with Policy Generators

https://github.com/jadkins99/hyperparameter_sensitivity

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A Method for Evaluating Hyperparameter Sensitivity in Reinforcement Learning

https://github.com/jaewoopudding/gta

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GTA: Generative Trajectory Augmentation with Guidance for Offline Reinforcement Learning

https://github.com/jaimesabalimperial/jaisalab

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Distributional constrained reinforcement learning for supply chain optimization

https://github.com/jakub-zarzycki2022/gattaca

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Graph Neural Network-Based Reinforcement Learning for Controlling Biological Networks: The GATTACA Framework

https://github.com/jan-schuchardt/learning-to-evolve

공식 코드

Learning to Evolve

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