Papers with Code 아카이브

General Reinforcement Learning

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 2
평가표: 6
보존된 평가 기록: 12

코드

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

https://github.com/dasgringuen/assetto_corsa_gym

공식 코드

A Simulation Benchmark for Autonomous Racing with Large-Scale Human Data

https://github.com/data-and-decision-lab/defix

공식 코드

DeFIX: Detecting and Fixing Failure Scenarios with Reinforcement Learning in Imitation Learning Based Autonomous Driving

https://github.com/data-science-in-mechanical-engineering/infoprop

공식 코드

On Rollouts in Model-Based Reinforcement Learning

https://github.com/david-friede/lddr

공식 코드

Learning Disentangled Discrete Representations

https://github.com/davidbrandfonbrener/onestep-rl

공식 코드

Offline RL Without Off-Policy Evaluation

https://github.com/decisionforce/EGPO

공식 코드

Safe Driving via Expert Guided Policy Optimization

https://github.com/deepmind/constrained_optidice

공식 코드

COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction Estimation

https://github.com/deepmind/deepmind-research/tree/master/rl_unplugged

공식 코드

RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning

https://github.com/deepmind/open_spiel

공식 코드

OpenSpiel: A Framework for Reinforcement Learning in Games

https://github.com/deepmind/rgb_stacking

공식 코드

Beyond Pick-and-Place: Tackling Robotic Stacking of Diverse Shapes

https://github.com/denisyarats/exorl

공식 코드

Don't Change the Algorithm, Change the Data: Exploratory Data for Offline Reinforcement Learning

https://github.com/desikrengarajan/fedora

공식 코드

Federated Ensemble-Directed Offline Reinforcement Learning

https://github.com/dido1998/CausalMBRL

공식 코드

Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning

https://github.com/djl11/ESM

공식 코드

End-to-End Egospheric Spatial Memory

https://github.com/dmksjfl/CABI

공식 코드

Double Check Your State Before Trusting It: Confidence-Aware Bidirectional Offline Model-Based Imagination

https://github.com/dmksjfl/seabo

공식 코드

SEABO: A Simple Search-Based Method for Offline Imitation Learning

https://github.com/dnandha/mopac

공식 코드

Model Predictive Actor-Critic: Accelerating Robot Skill Acquisition with Deep Reinforcement Learning

https://github.com/dohyeoklee/SPQR

공식 코드

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

https://github.com/dotchen/WorldOnRails

공식 코드

Learning to drive from a world on rails

https://github.com/dragon-zhuang/bppo

공식 코드

Behavior Proximal Policy Optimization

https://github.com/dragon-zhuang/reinformer

공식 코드

Reinformer: Max-Return Sequence Modeling for Offline RL

https://github.com/dsshim0125/s2p

공식 코드

S2P: State-conditioned Image Synthesis for Data Augmentation in Offline Reinforcement Learning

https://github.com/dt6a/actoreg

공식 코드

The Role of Deep Learning Regularizations on Actors in Offline RL

https://github.com/dt6a/clorl

공식 코드

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

https://github.com/dt6a/rebrac

공식 코드

Revisiting the Minimalist Approach to Offline Reinforcement Learning

https://github.com/dtak/mbrl-smdp-ode

공식 코드

Model-based Reinforcement Learning for Semi-Markov Decision Processes with Neural ODEs

https://github.com/ed2-source-code/ed2

공식 코드

ED2: Environment Dynamics Decomposition World Models for Continuous Control

https://github.com/egg-west/mcep

공식 코드

Mildly Constrained Evaluation Policy for Offline Reinforcement Learning

https://github.com/eleurent/highway-env

공식 코드

Understanding and Mitigating the Limitations of Prioritized Experience Replay

https://github.com/elicassion/StARformer

공식 코드

StARformer: Transformer with State-Action-Reward Representations for Visual Reinforcement Learning

https://github.com/emptyjackson/unifloral

공식 코드

A Clean Slate for Offline Reinforcement Learning

https://github.com/enajx/ndp

공식 코드

Towards Self-Assembling Artificial Neural Networks through Neural Developmental Programs

https://github.com/enjeeneer/conservative-world-models

공식 코드

Zero-Shot Reinforcement Learning from Low Quality Data

https://github.com/enjeeneer/zero-shot-rl

공식 코드

Zero-Shot Reinforcement Learning from Low Quality Data

https://github.com/erg0dic/transmon_public

공식 코드

Sample-efficient Model-based Reinforcement Learning for Quantum Control

https://github.com/eric-mitchell/macaw

공식 코드

Offline Meta-Reinforcement Learning with Advantage Weighting

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.