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/mlpc-ucsd/xtra

공식 코드

On the Feasibility of Cross-Task Transfer with Model-Based Reinforcement Learning

https://github.com/moreanp/osc

공식 코드

Policy Constraint by Only Support Constraint for Offline Reinforcement Learning

https://github.com/motokiomura/MXQL

공식 코드

Stabilizing Extreme Q-learning by Maclaurin Expansion

https://github.com/mrjun123/DPETS

공식 코드

Practical Probabilistic Model-based Deep Reinforcement Learning by Integrating Dropout Uncertainty and Trajectory Sampling

https://github.com/mxu34/mbrl-gpmm

공식 코드

Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian Processes

https://github.com/nakamotoo/cal-ql

공식 코드

Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuning

https://github.com/natashamjaques/neural_chat

공식 코드

Human-centric Dialog Training via Offline Reinforcement Learning

https://github.com/nicoladainese96/code-world-models

공식 코드

Generating Code World Models with Large Language Models Guided by Monte Carlo Tree Search

https://github.com/nicolinho/dutd

공식 코드

Dynamic Update-to-Data Ratio: Minimizing World Model Overfitting

https://github.com/notmahi/bet

공식 코드

Behavior Transformers: Cloning $k$ modes with one stone

https://github.com/nusnlp/prs

공식 코드

Preference-Guided Reflective Sampling for Aligning Language Models

https://github.com/nzolman/sindy-rl

공식 코드

SINDy-RL: Interpretable and Efficient Model-Based Reinforcement Learning

https://github.com/oh-lab/count-morl

공식 코드

Model-based Offline Reinforcement Learning with Count-based Conservatism

https://github.com/openai/procgen

공식 코드

Differentiable Tree Search Network

https://github.com/opendilab/DI-engine

공식 코드

A Perspective of Q-value Estimation on Offline-to-Online Reinforcement Learning

https://github.com/opendilab/GenerativeRL

공식 코드

Revisiting Generative Policies: A Simpler Reinforcement Learning Algorithmic Perspective

https://github.com/pairlab/vagram

공식 코드

Value Gradient weighted Model-Based Reinforcement Learning

https://github.com/panmt/mbrl_with_isolated_imaginations

공식 코드

Model-Based Reinforcement Learning with Isolated Imaginations

https://github.com/panmt/vid2act

공식 코드

Model-Based Reinforcement Learning with Multi-Task Offline Pretraining

https://github.com/pbecker93/vrkn

공식 코드

On Uncertainty in Deep State Space Models for Model-Based Reinforcement Learning

https://github.com/pcheng2/tsrl

공식 코드

Look Beneath the Surface: Exploiting Fundamental Symmetry for Sample-Efficient Offline RL

https://github.com/pengyang7881187/sicmdp-new

공식 코드

Semi-Infinitely Constrained Markov Decision Processes and Efficient Reinforcement Learning

https://github.com/penn-pal-lab/robot-aware-control

공식 코드

Know Thyself: Transferable Visual Control Policies Through Robot-Awareness

https://github.com/peteanderson80/Matterport3DSimulator

공식 코드

Look Before You Leap: Bridging Model-Free and Model-Based Reinforcement Learning for Planned-Ahead Vision-and-Language Navigation

https://github.com/philippe-eecs/idql

공식 코드

IDQL: Implicit Q-Learning as an Actor-Critic Method with Diffusion Policies

https://github.com/pikaju/muzero-g

공식 코드

Improving Model-Based Reinforcement Learning with Internal State Representations through Self-Supervision

https://github.com/pipixiaqishi1/tatu

공식 코드

Uncertainty-driven Trajectory Truncation for Data Augmentation in Offline Reinforcement Learning

https://github.com/pku-marl/dexteroushands

공식 코드

Towards Human-Level Bimanual Dexterous Manipulation with Reinforcement Learning

https://github.com/polixir/NeoRL

공식 코드

NeoRL: A Near Real-World Benchmark for Offline Reinforcement Learning

https://github.com/polixir/neorl2

공식 코드

NeoRL-2: Near Real-World Benchmarks for Offline Reinforcement Learning with Extended Realistic Scenarios

https://github.com/prajjwal1/rl_paradigm

공식 코드

When should we prefer Decision Transformers for Offline Reinforcement Learning?

https://github.com/proppo/pipps_demo

공식 코드

PIPPS: Flexible Model-Based Policy Search Robust to the Curse of Chaos

https://github.com/purewater0901/BSMPC

공식 코드

Bisimulation metric for Model Predictive Control

https://github.com/purewater0901/scq

공식 코드

Strategically Conservative Q-Learning

https://github.com/qianlin04/prmorl

공식 코드

Policy-regularized Offline Multi-objective Reinforcement Learning

https://github.com/qinwenluo/ssar

공식 코드

Learning to Trust Bellman Updates: Selective State-Adaptive Regularization for Offline RL

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