Papers with Code 아카이브

General Reinforcement Learning

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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/thanhnguyentang/neural-offline-rl

공식 코드

VIPeR: Provably Efficient Algorithm for Offline RL with Neural Function Approximation

https://github.com/thinkwee/nover

공식 코드

NOVER: Incentive Training for Language Models via Verifier-Free Reinforcement Learning

https://github.com/thobotics/RoMBRL

공식 코드

Bayes-Adaptive Deep Model-Based Policy Optimisation

https://github.com/thomasbi1/cyberrunner

공식 코드

Sample-Efficient Learning to Solve a Real-World Labyrinth Game Using Data-Augmented Model-Based Reinforcement Learning

https://github.com/thu-rllab/CFCQL

공식 코드

Counterfactual Conservative Q Learning for Offline Multi-agent Reinforcement Learning

https://github.com/thuml/CompilerDream

공식 코드

CompilerDream: Learning a Compiler World Model for General Code Optimization

https://github.com/thuml/ContextWM

공식 코드

Pre-training Contextualized World Models with In-the-wild Videos for Reinforcement Learning

https://github.com/thuml/Flowformer

공식 코드

Flowformer: Linearizing Transformers with Conservation Flows

https://github.com/thuml/SPOT

공식 코드

Supported Policy Optimization for Offline Reinforcement Learning

https://github.com/thuml/harmonydream

공식 코드

HarmonyDream: Task Harmonization Inside World Models

https://github.com/thuml/iVideoGPT

공식 코드

iVideoGPT: Interactive VideoGPTs are Scalable World Models

https://github.com/tiancigao/diffppo

공식 코드

Enhancing Sample Efficiency and Exploration in Reinforcement Learning through the Integration of Diffusion Models and Proximal Policy Optimization

https://github.com/tianheyu927/mopo

공식 코드

MOPO: Model-based Offline Policy Optimization

https://github.com/tianyucodings/diffusion_trusted_q_learning

공식 코드

Diffusion Policies creating a Trust Region for Offline Reinforcement Learning

https://github.com/tinkoff-ai/cnf

공식 코드

Let Offline RL Flow: Training Conservative Agents in the Latent Space of Normalizing Flows

https://github.com/tmoer/multimodal_varinf

공식 코드

Learning Multimodal Transition Dynamics for Model-Based Reinforcement Learning

https://github.com/tsutikgiau/valuememorygraph

공식 코드

Value Memory Graph: A Graph-Structured World Model for Offline Reinforcement Learning

https://github.com/tumcps/pytupli

공식 코드

PyTupli: A Scalable Infrastructure for Collaborative Offline Reinforcement Learning Projects

https://github.com/tung-nd/TPC-tensorflow

공식 코드

Temporal Predictive Coding For Model-Based Planning In Latent Space

https://github.com/tung-nd/cwbc

공식 코드

Reliable Conditioning of Behavioral Cloning for Offline Reinforcement Learning

https://github.com/uoe-agents/imitation-learning-pcg

공식 코드

Using Offline Data to Speed Up Reinforcement Learning in Procedurally Generated Environments

https://github.com/uoe-agents/robust_onpolicy_data_collection

공식 코드

Robust On-Policy Sampling for Data-Efficient Policy Evaluation in Reinforcement Learning

https://github.com/uscresl/dmfd

공식 코드

Learning Deformable Object Manipulation from Expert Demonstrations

https://github.com/utiasstars/visual-haptic-dynamics

공식 코드

Learning Sequential Latent Variable Models from Multimodal Time Series Data

https://github.com/valarzz/dlpa

공식 코드

Model-based Reinforcement Learning for Parameterized Action Spaces

https://github.com/valarzz/model-based-reinforcement-learning-for-parameterized-action-spaces

공식 코드

Model-based Reinforcement Learning for Parameterized Action Spaces

https://github.com/valeoai/rainbow-iqn-apex

공식 코드

Is Deep Reinforcement Learning Really Superhuman on Atari? Leveling the playing field

https://github.com/vanderschaarlab/neurallaplacecontrol

공식 코드

Neural Laplace Control for Continuous-time Delayed Systems

https://github.com/vermouth1992/bracp

공식 코드

BRAC+: Improved Behavior Regularized Actor Critic for Offline Reinforcement Learning

https://github.com/vincentzhang/decentcem

공식 코드

A Simple Decentralized Cross-Entropy Method

https://github.com/vineetjain96/merlin

공식 코드

Learning to Reach Goals via Diffusion

https://github.com/vision-cair/af-guide

공식 코드

Guiding Online Reinforcement Learning with Action-Free Offline Pretraining

https://github.com/vmartinezalvarez/DyNODE

공식 코드

DyNODE: Neural Ordinary Differential Equations for Dynamics Modeling in Continuous Control

https://github.com/vvanirudh/lamps-mbrl

공식 코드

The Virtues of Laziness in Model-based RL: A Unified Objective and Algorithms

https://github.com/wadx2019/qvpo

공식 코드

Diffusion-based Reinforcement Learning via Q-weighted Variational Policy Optimization

https://github.com/wangzizhao/causaldynamicslearning

공식 코드

Causal Dynamics Learning for Task-Independent State Abstraction

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