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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/rohanpsingh/learninghumanoidwalking

공식 코드

Learning Bipedal Walking On Planned Footsteps For Humanoid Robots

https://github.com/roman1e2f5p8s/rlapseingym

공식 코드

Reinforcement Learning with Algorithms from Probabilistic Structure Estimation

https://github.com/romanbelaire/acoe-robust-rl

공식 코드

Probabilistic Perspectives on Error Minimization in Adversarial Reinforcement Learning

https://github.com/romanbelaire/robust-ccer

공식 코드

Regret-Based Defense in Adversarial Reinforcement Learning

https://github.com/romina72/paml

공식 코드

Policy-Aware Model Learning for Policy Gradient Methods

https://github.com/rommeoijcai2019/rommeo

공식 코드

A Regularized Opponent Model with Maximum Entropy Objective

https://github.com/ron-amit/Discount_as_Regularizer

공식 코드

Discount Factor as a Regularizer in Reinforcement Learning

https://github.com/ronaldosvieira/gym-locm

공식 코드

Mastering Strategy Card Game (Legends of Code and Magic) via End-to-End Policy and Optimistic Smooth Fictitious Play

https://github.com/ronjafuchs/ice_ai

공식 코드

Personalized Dynamic Difficulty Adjustment -- Imitation Learning Meets Reinforcement Learning

https://github.com/roosephu/boots

공식 코드

On the Expressivity of Neural Networks for Deep Reinforcement Learning

https://github.com/roosephu/slbo

공식 코드

Algorithmic Framework for Model-based Deep Reinforcement Learning with Theoretical Guarantees

https://github.com/roozbehrazavi/bimrl

공식 코드

BIMRL: Brain Inspired Meta Reinforcement Learning

https://github.com/rosewang2008/rmaddpg

공식 코드

R-MADDPG for Partially Observable Environments and Limited Communication

https://github.com/rotmanfinhub/gamma-vega-rl-hedging

공식 코드

Gamma and Vega Hedging Using Deep Distributional Reinforcement Learning

https://github.com/rotskoff-group/marl-design

공식 코드

Cooperative multi-agent reinforcement learning for high-dimensional nonequilibrium control

https://github.com/roythuly/brhpo

공식 코드

Bidirectional-Reachable Hierarchical Reinforcement Learning with Mutually Responsive Policies

https://github.com/rpatrik96/AttA2C

공식 코드

Attention-based Curiosity-driven Exploration in Deep Reinforcement Learning

https://github.com/rr-learning/trifinger-rl-example

공식 코드

Benchmarking Offline Reinforcement Learning on Real-Robot Hardware

https://github.com/rr-learning/trifinger_rl_datasets

공식 코드

Benchmarking Offline Reinforcement Learning on Real-Robot Hardware

https://github.com/rraileanu/auto-drac

공식 코드

Automatic Data Augmentation for Generalization in Deep Reinforcement Learning

https://github.com/rraileanu/idaac

공식 코드

Decoupling Value and Policy for Generalization in Reinforcement Learning

https://github.com/rsantana-isg/morl_benchmark

공식 코드

Benchmarking MOEAs for solving continuous multi-objective RL problems

https://github.com/rschiewer/lrdm

공식 코드

Modular Networks Prevent Catastrophic Interference in Model-Based Multi-Task Reinforcement Learning

https://github.com/rssalessio/modelfreeactivateexplorationrl

공식 코드

Model-Free Active Exploration in Reinforcement Learning

https://github.com/rstrudel/rlbc

공식 코드

Learning to combine primitive skills: A step towards versatile robotic manipulation

https://github.com/rtealwitter/openstreets

공식 코드

I Open at the Close: A Deep Reinforcement Learning Evaluation of Open Streets Initiatives

https://github.com/rtloftin/hi-c

공식 코드

Uncoupled Learning of Differential Stackelberg Equilibria with Commitments

https://github.com/rubai5/ESS_Game

공식 코드

Can Deep Reinforcement Learning Solve Erdos-Selfridge-Spencer Games?

https://github.com/rubenrtorrado/GVGAI_GYM

공식 코드

Deep Reinforcement Learning for General Video Game AI

https://github.com/ruihuang1967/Deep-GRAIL

공식 코드

Rate-Splitting for Intelligent Reflecting Surface-Aided Multiuser VR Streaming

https://github.com/ruizhaogit/EnergyBasedPrioritization

공식 코드

Energy-Based Hindsight Experience Prioritization

https://github.com/ruizhaogit/GuessWhat-TemperedPolicyGradient

공식 코드

Learning Goal-Oriented Visual Dialog via Tempered Policy Gradient

https://github.com/ruizhaogit/MNIST-GuessNumber

공식 코드

Efficient Dialog Policy Learning via Positive Memory Retention

https://github.com/ruizhaogit/PositiveMemoryRetention

공식 코드

Efficient Dialog Policy Learning via Positive Memory Retention

https://github.com/ruizhaogit/mep

공식 코드

Maximum Entropy-Regularized Multi-Goal Reinforcement Learning

https://github.com/ruizheng20/gpo

공식 코드

Toward Optimal LLM Alignments Using Two-Player Games

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