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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/tuomaso/radial_rl

공식 코드

Robust Deep Reinforcement Learning through Adversarial Loss

https://github.com/tuomaso/radial_rl_v2

공식 코드

Robust Deep Reinforcement Learning through Adversarial Loss

https://github.com/turningpoint-ai/visualthinker-r1-zero

공식 코드

R1-Zero's "Aha Moment" in Visual Reasoning on a 2B Non-SFT Model

https://github.com/twni2016/f-IRL

공식 코드

f-IRL: Inverse Reinforcement Learning via State Marginal Matching

https://github.com/twni2016/self-predictive-rl

공식 코드

Bridging State and History Representations: Understanding Self-Predictive RL

https://github.com/two2tee/WorldModelPlanning

공식 코드

Evolutionary Planning in Latent Space

https://github.com/tyler-ingebrand/functionencoderrl

공식 코드

Zero-Shot Reinforcement Learning via Function Encoders

https://github.com/tylerkastner/distribution-equivalence

공식 코드

Distributional Model Equivalence for Risk-Sensitive Reinforcement Learning

https://github.com/typoverflow/wiserl

공식 코드

Hindsight Preference Learning for Offline Preference-based Reinforcement Learning

https://github.com/tyq1024/rlx2

공식 코드

RLx2: Training a Sparse Deep Reinforcement Learning Model from Scratch

https://github.com/uber-common/deep-neuroevolution

공식 코드

VINE: An Open Source Interactive Data Visualization Tool for Neuroevolution

https://github.com/uber-common/differentiable-plasticity

공식 코드

Differentiable plasticity: training plastic neural networks with backpropagation

https://github.com/uber-common/safemutations

공식 코드

Safe Mutations for Deep and Recurrent Neural Networks through Output Gradients

https://github.com/uber-research/TuRBO

공식 코드

Scalable Global Optimization via Local Bayesian Optimization

https://github.com/uber-research/atari-model-zoo

공식 코드

An Atari Model Zoo for Analyzing, Visualizing, and Comparing Deep Reinforcement Learning Agents

https://github.com/uber-research/deep-neuroevolution

공식 코드

Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-Seeking Agents

https://github.com/uber-research/go-explore

공식 코드

First return, then explore

https://github.com/uber-research/normative-uncertainty

공식 코드

Reinforcement Learning Under Moral Uncertainty

https://github.com/uber-research/plato-research-dialogue-system

공식 코드

Collaborative Multi-Agent Dialogue Model Training Via Reinforcement Learning

https://github.com/uber-research/poet

공식 코드

Enhanced POET: Open-Ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their Solutions

https://github.com/ubi-coro/precise_pushing

공식 코드

Precision-Focused Reinforcement Learning Model for Robotic Object Pushing

https://github.com/ubisoft/directbehaviorspecification

공식 코드

Direct Behavior Specification via Constrained Reinforcement Learning

https://github.com/ubisoft/ubisoft-la-forge-ASAF

공식 코드

Adversarial Soft Advantage Fitting: Imitation Learning without Policy Optimization

https://github.com/ucamrl/xrlflow

공식 코드

X-RLflow: Graph Reinforcement Learning for Neural Network Subgraphs Transformation

https://github.com/ucb-bar/autophase

공식 코드

AutoPhase: Juggling HLS Phase Orderings in Random Forests with Deep Reinforcement Learning

https://github.com/ucbdrive/skipnet

공식 코드

SkipNet: Learning Dynamic Routing in Convolutional Networks

https://github.com/ucbtns/dai

공식 코드

Active inference: demystified and compared

https://github.com/ucl-dark/skillhack

공식 코드

Hierarchical Kickstarting for Skill Transfer in Reinforcement Learning

https://github.com/ucl-ift/noisyenv

공식 코드

Simple Noisy Environment Augmentation for Reinforcement Learning

https://github.com/ucuapps/modelicagym

공식 코드

ModelicaGym: Applying Reinforcement Learning to Modelica Models

https://github.com/udeshmg/gta-rl

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Solving Dynamic Graph Problems with Multi-Attention Deep Reinforcement Learning

https://github.com/ugr-sail/paper-drl_building

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An experimental evaluation of Deep Reinforcement Learning algorithms for HVAC control

https://github.com/ugurkanates/NeurIRS2019DroneChallengeRL

공식 코드

Long-Term Planning with Deep Reinforcement Learning on Autonomous Drones

https://github.com/uicdice/imm-logistic-regression

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Induced Model Matching: How Restricted Models Can Help Larger Ones

https://github.com/ulieu/rl_patchy

공식 코드

Dynamic control of self-assembly of quasicrystalline structures through reinforcement learning

https://github.com/umass-ml4ed/reticl

공식 코드

RetICL: Sequential Retrieval of In-Context Examples with Reinforcement Learning

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