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 3476–3500 of 15113 papers

TitleStatusHype
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
TreeC: a method to generate interpretable energy management systems using a metaheuristic algorithmCode0
MDDL: A Framework for Reinforcement Learning-based Position Allocation in Multi-Channel Feed—0
Bandit-Based Policy Invariant Explicit Shaping for Incorporating External Advice 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
Minimax-Optimal Reward-Agnostic Exploration in Reinforcement Learning—0
Car-Following Models: A Multidisciplinary Review—0
Language Instructed Reinforcement Learning for Human-AI CoordinationCode1
Model-based Dynamic Shielding for Safe and Efficient Multi-Agent Reinforcement Learning—0
Facilitating Sim-to-real by Intrinsic Stochasticity of Real-Time Simulation in Reinforcement Learning for Robot Manipulation—0
Multi-agent Policy Reciprocity with Theoretical Guarantee—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
Optimal Interpretability-Performance Trade-off of Classification Trees with Black-Box Reinforcement Learning—0
Feudal Graph Reinforcement LearningCode0
RESPECT: Reinforcement Learning based Edge Scheduling on Pipelined Coral Edge TPUsCode1
Uncertainty-driven Trajectory Truncation for Data Augmentation in Offline Reinforcement LearningCode0
For Pre-Trained Vision Models in Motor Control, Not All Policy Learning Methods are Created Equal—0
Learning a Universal Human Prior for Dexterous Manipulation from Human Preference—0
Eagle: End-to-end Deep Reinforcement Learning based Autonomous Control of PTZ CamerasCode1
AI-Driven Resource Allocation in Optical Wireless Communication Systems—0
Stable and Safe Reinforcement Learning via a Barrier-Lyapunov Actor-Critic ApproachCode1
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Benchmark Results

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