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 5076–5100 of 15113 papers

TitleStatusHype
Reinforcement Learning Approaches for Traffic Signal Control under Missing DataCode0
A Cubic-regularized Policy Newton Algorithm for Reinforcement Learning—0
A Review of Symbolic, Subsymbolic and Hybrid Methods for Sequential Decision Making—0
End-to-End Policy Gradient Method for POMDPs and Explainable Agents—0
Learning and Adapting Agile Locomotion Skills by Transferring Experience—0
FastRLAP: A System for Learning High-Speed Driving via Deep RL and Autonomous Practicing—0
Cooperative Multi-Agent Reinforcement Learning for Inventory Management—0
Feasible Policy Iteration for Safe Reinforcement Learning—0
Provably Feedback-Efficient Reinforcement Learning via Active Reward Learning—0
An adaptive safety layer with hard constraints for safe reinforcement learning in multi-energy management systems—0
Using Offline Data to Speed Up Reinforcement Learning in Procedurally Generated EnvironmentsCode0
MDDL: A Framework for Reinforcement Learning-based Position Allocation in Multi-Channel Feed—0
TreeC: a method to generate interpretable energy management systems using a metaheuristic algorithmCode0
Minimax-Optimal Reward-Agnostic Exploration in Reinforcement Learning—0
Exploring the Noise Resilience of Successor Features and Predecessor Features Algorithms in One and Two-Dimensional Environments—0
Towards Controllable Diffusion Models via Reward-Guided Exploration—0
Car-Following Models: A Multidisciplinary Review—0
Bandit-Based Policy Invariant Explicit Shaping for Incorporating External Advice in Reinforcement Learning—0
Model-based Dynamic Shielding for Safe and Efficient Multi-Agent Reinforcement Learning—0
Multi-agent Policy Reciprocity with Theoretical Guarantee—0
Facilitating Sim-to-real by Intrinsic Stochasticity of Real-Time Simulation in Reinforcement Learning for Robot Manipulation—0
Human-Robot Skill Transfer with Enhanced Compliance via Dynamic Movement Primitives—0
Did we personalize? Assessing personalization by an online reinforcement learning algorithm using resamplingCode0
Control invariant set enhanced reinforcement learning for process control: improved sampling efficiency and guaranteed stability—0
Feudal Graph Reinforcement LearningCode0
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Benchmark Results

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