SOTAVerified

Reinforcement Learning (RL)

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.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1776–1800 of 15113 papers

TitleStatusHype
ROLeR: Effective Reward Shaping in Offline Reinforcement Learning for Recommender SystemsCode0
Random Latent Exploration for Deep Reinforcement Learning—0
Understanding Reinforcement Learning-Based Fine-Tuning of Diffusion Models: A Tutorial and ReviewCode2
Sparsity-based Safety Conservatism for Constrained Offline Reinforcement Learning—0
Chip Placement with Diffusion ModelsCode1
Energy-Guided Diffusion Sampling for Offline-to-Online Reinforcement LearningCode1
Variable-Agnostic Causal Exploration for Reinforcement LearningCode1
A Graph-based Adversarial Imitation Learning Framework for Reliable & Realtime Fleet Scheduling in Urban Air Mobility—0
Deflated Dynamics Value Iteration—0
Balancing the Scales: Reinforcement Learning for Fair ClassificationCode0
GuideLight: "Industrial Solution" Guidance for More Practical Traffic Signal Control AgentsCode0
SuperPADL: Scaling Language-Directed Physics-Based Control with Progressive Supervised Distillation—0
Reinforcement Learning in High-frequency Market MakingCode1
Affordance-Guided Reinforcement Learning via Visual Prompting—0
Learning to Steer Markovian Agents under Model UncertaintyCode0
Deep reinforcement learning with symmetric data augmentation applied for aircraft lateral attitude tracking control—0
Global Reinforcement Learning: Beyond Linear and Convex Rewards via Submodular Semi-gradient Methods—0
Communication-Aware Reinforcement Learning for Cooperative Adaptive Cruise Control—0
A Benchmark Environment for Offline Reinforcement Learning in Racing GamesCode1
Transductive Active Learning with Application to Safe Bayesian OptimizationCode1
PID Accelerated Temporal Difference Algorithms—0
Enhancing Performance and User Engagement in Everyday Stress Monitoring: A Context-Aware Active Reinforcement Learning Approach—0
A Review of Nine Physics Engines for Reinforcement Learning Research—0
Gradient Boosting Reinforcement LearningCode2
Token-Mol 1.0: Tokenized drug design with large language model—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76—Unverified
2PPOMean Normalized Performance0.58—Unverified