Papers with Code 아카이브

Reinforcement Learning

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

1

평가표

162

보존된 평가 기록

1

코드

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

https://github.com/nyu-dl/dl4mt-cdec

공식 코드

Plan, Attend, Generate: Planning for Sequence-to-Sequence Models

https://github.com/nyu-larx/attack_metarl

공식 코드

Sampling Attacks on Meta Reinforcement Learning: A Minimax Formulation and Complexity Analysis

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

공식 코드

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

https://github.com/o-ran-sc/sim-ns3-o-ran-e2

공식 코드

Programmable and Customized Intelligence for Traffic Steering in 5G Networks Using Open RAN Architectures

https://github.com/ocram17/treenhance

공식 코드

TreEnhance: A Tree Search Method For Low-Light Image Enhancement

https://github.com/offdynamicsrl/off-dynamics-rl

공식 코드

ODRL: A Benchmark for Off-Dynamics Reinforcement Learning

https://github.com/offworld-projects/research-halgan

공식 코드

Addressing Sample Complexity in Visual Tasks Using HER and Hallucinatory GANs

https://github.com/ofsoundof/dhp

공식 코드

DHP: Differentiable Meta Pruning via HyperNetworks

https://github.com/oh-lab/APPO

공식 코드

Adversarial Policy Optimization for Offline Preference-based Reinforcement Learning

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

공식 코드

Model-based Offline Reinforcement Learning with Count-based Conservatism

https://github.com/oist-cnru/ReMASTER

공식 코드

Self-organization of action hierarchy and compositionality by reinforcement learning with recurrent neural networks

https://github.com/oist-cnru/Variational-Recurrent-Models

공식 코드

Variational Recurrent Models for Solving Partially Observable Control Tasks

https://github.com/oj9040/GraphSAGE_RL

공식 코드

Advancing GraphSAGE with A Data-Driven Node Sampling

https://github.com/om-ai-lab/vlm-r1

공식 코드

VLM-R1: A Stable and Generalizable R1-style Large Vision-Language Model

https://github.com/omarathon/rl-multi-agent-car-parking

공식 코드

Multi-Agent Car Parking using Reinforcement Learning

https://github.com/omardrwch/kernel_ucbvi_experiments

공식 코드

Kernel-Based Reinforcement Learning: A Finite-Time Analysis

https://github.com/omarshindi/model-free-quantum-gate-design-and-calibration-using-deep-reinforcement-learning

공식 코드

Model-free Quantum Gate Design and Calibration using Deep Reinforcement Learning

https://github.com/omikad/halfweg

공식 코드

Solving Sokoban using Hierarchical Reinforcement Learning with Landmarks

https://github.com/omron-sinicx/action-constrained-rl-benchmark

공식 코드

Benchmarking Actor-Critic Deep Reinforcement Learning Algorithms for Robotics Control with Action Constraints

https://github.com/omron-sinicx/symmetry-aware-pomdp

공식 코드

Symmetry-aware Reinforcement Learning for Robotic Assembly under Partial Observability with a Soft Wrist

https://github.com/ondrejba/discrete_abstractions

공식 코드

Learning Discrete State Abstractions With Deep Variational Inference

https://github.com/onizukalab/spatial-data-partitioning-using-drl

공식 코드

Learned spatial data partitioning

https://github.com/open-thought/reasoning-gym

공식 코드

REASONING GYM: Reasoning Environments for Reinforcement Learning with Verifiable Rewards

https://github.com/openai/EPG

공식 코드

Evolved Policy Gradients

https://github.com/openai/baselines

공식 코드

Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation

https://github.com/openai/coinrun

공식 코드

Quantifying Generalization in Reinforcement Learning

https://github.com/openai/evolution-strategies-starter

공식 코드

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/openai/large-scale-curiosity

공식 코드

Large-Scale Study of Curiosity-Driven Learning

https://github.com/openai/lm-human-preferences

공식 코드

Fine-Tuning Language Models from Human Preferences

https://github.com/openai/mlsh

공식 코드

Meta Learning Shared Hierarchies

https://github.com/openai/multi-agent-emergence-environments

공식 코드

Emergent Tool Use From Multi-Agent Autocurricula

https://github.com/openai/multiagent-competition

공식 코드

Emergent Complexity via Multi-Agent Competition

https://github.com/openai/multiagent-particle-envs

공식 코드

Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments

https://github.com/openai/procgen

공식 코드

Leveraging Procedural Generation to Benchmark Reinforcement Learning

https://github.com/openai/random-network-distillation

공식 코드

Exploration by Random Network Distillation

https://github.com/openai/train-procgen

공식 코드

Leveraging Procedural Generation to Benchmark Reinforcement Learning

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