Papers with Code 아카이브

Reinforcement Learning (RL)

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 6
평가표: 2
보존된 평가 기록: 3

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

코드

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

https://github.com/charlie0257/hytl

공식 코드

Exploiting Hybrid Policy in Reinforcement Learning for Interpretable Temporal Logic Manipulation

https://github.com/charlie0257/t2tl

공식 코드

Exploiting Transformer in Sparse Reward Reinforcement Learning for Interpretable Temporal Logic Motion Planning

https://github.com/chauncygu/multi-agent-constrained-policy-optimisation

공식 코드

Multi-Agent Constrained Policy Optimisation

https://github.com/chauncygu/safe-reinforcement-learning-baselines

공식 코드

A Review of Safe Reinforcement Learning: Methods, Theory and Applications

https://github.com/chen-gx/seer

공식 코드

SEER: Facilitating Structured Reasoning and Explanation via Reinforcement Learning

https://github.com/chendrag/sfbc

공식 코드

Offline Reinforcement Learning via High-Fidelity Generative Behavior Modeling

https://github.com/chenguoz/caig

공식 코드

CTR-Driven Advertising Image Generation with Multimodal Large Language Models

https://github.com/chengyanggu/neuralpmp2024

공식 코드

Pontryagin Optimal Control via Neural Networks

https://github.com/chenhongge/StateAdvDRL

공식 코드

Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations

https://github.com/chenllliang/g1

공식 코드

G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement Learning

https://github.com/chennnnnyize/powersystem_rl_attacks

공식 코드

Understanding the Safety Requirements for Learning-based Power Systems Operations

https://github.com/chenwang4/oilad

공식 코드

OIL-AD: An Anomaly Detection Framework for Sequential Decision Sequences

https://github.com/chenxi116/PNASNet.TF

공식 코드

Progressive Neural Architecture Search

https://github.com/chenxi116/PNASNet.pytorch

공식 코드

Progressive Neural Architecture Search

https://github.com/chenxuerun/APS

공식 코드

Adjust Planning Strategies to Accommodate Reinforcement Learning Agents

https://github.com/chenyiqun/mmoa-rag

공식 코드

Improving Retrieval-Augmented Generation through Multi-Agent Reinforcement Learning

https://github.com/chenyuwang-monica/drakes

공식 코드

Fine-Tuning Discrete Diffusion Models via Reward Optimization with Applications to DNA and Protein Design

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

공식 코드

Reinforcement Learning Generalization with Surprise Minimization

https://github.com/chijames/gst

공식 코드

Training Discrete Deep Generative Models via Gapped Straight-Through Estimator

https://github.com/chingyaoc/DCL

공식 코드

Debiased Contrastive Learning

https://github.com/chkwon/TSPDrone-RL

공식 코드

A Deep Reinforcement Learning Approach for Solving the Traveling Salesman Problem with Drone

https://github.com/chongminggao/dorl-codes

공식 코드

Alleviating Matthew Effect of Offline Reinforcement Learning in Interactive Recommendation

https://github.com/chongminggao/easyrl4rec

공식 코드

EasyRL4Rec: An Easy-to-use Library for Reinforcement Learning Based Recommender Systems

https://github.com/chongyi-zheng/infom

공식 코드

Intention-Conditioned Flow Occupancy Models

https://github.com/chongyi-zheng/stable_contrastive_rl

공식 코드

Stabilizing Contrastive RL: Techniques for Robotic Goal Reaching from Offline Data

https://github.com/chriscameron1/mcfs

공식 코드

UNSAT Solver Synthesis via Monte Carlo Forest Search

https://github.com/chrisgrimm/proper_value_equivalence

공식 코드

Proper Value Equivalence

https://github.com/christianll9/tsppc-drl

공식 코드

Solving the Traveling Salesperson Problem with Precedence Constraints by Deep Reinforcement Learning

https://github.com/christoffeldoorman/network-rewiring-rl

공식 코드

Dynamic Network Reconfiguration for Entropy Maximization using Deep Reinforcement Learning

https://github.com/christopher-hsu/scalableMARL

공식 코드

Scalable Reinforcement Learning Policies for Multi-Agent Control

https://github.com/chriswang98sz/bierl

공식 코드

BiERL: A Meta Evolutionary Reinforcement Learning Framework via Bilevel Optimization

https://github.com/chrodan/FiniteEpisodicRL.jl

공식 코드

Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement Learning

https://github.com/chwoong/lire

공식 코드

Listwise Reward Estimation for Offline Preference-based Reinforcement Learning

https://github.com/chxu2000/bqsched

공식 코드

BQSched: A Non-intrusive Scheduler for Batch Concurrent Queries via Reinforcement Learning

https://github.com/chz056/bear

공식 코드

BEAR: Physics-Principled Building Environment for Control and Reinforcement Learning

https://github.com/cipollone/multinav2

공식 코드

Exploiting Multiple Abstractions in Episodic RL via Reward Shaping

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