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 3401–3425 of 15113 papers

TitleStatusHype
A Novel Reinforcement Learning Model for Post-Incident Malware Investigations—0
Action abstractions for amortized sampling—0
A Large Language Model-Driven Reward Design Framework via Dynamic Feedback for Reinforcement Learning—0
Harnessing Causality in Reinforcement Learning With Bagged Decision Times—0
Reinforcement Learning in Non-Markov Market-Making—0
Interpretable end-to-end Neurosymbolic Reinforcement Learning agents—0
Towards Effective Planning Strategies for Dynamic Opinion NetworksCode0
Guided Reinforcement Learning for Robust Multi-Contact Loco-Manipulation—0
ORSO: Accelerating Reward Design via Online Reward Selection and Policy OptimizationCode0
MarineFormer: A Spatio-Temporal Attention Model for USV Navigation in Dynamic Marine Environments—0
Coordinated Dispatch of Energy Storage Systems in the Active Distribution Network: A Complementary Reinforcement Learning and Optimization Approach—0
Integrating Large Language Models and Reinforcement Learning for Non-Linear Reasoning—0
Truncating Trajectories in Monte Carlo Policy Evaluation: an Adaptive Approach—0
Dynamic Learning Rate for Deep Reinforcement Learning: A Bandit Approach—0
Augmented Intelligence in Smart Intersections: Local Digital Twins-Assisted Hybrid Autonomous Driving—0
EdgeRL: Reinforcement Learning-driven Deep Learning Model Inference Optimization at Edge—0
Reinforcement Learning with Euclidean Data Augmentation for State-Based Continuous ControlCode0
When to Trust Your Data: Enhancing Dyna-Style Model-Based Reinforcement Learning With Data Filter—0
Neural-based Control for CubeSat Docking Maneuvers—0
SAC-GLAM: Improving Online RL for LLM agents with Soft Actor-Critic and Hindsight Relabeling—0
Reinforcement Learning with LTL and ω-Regular Objectives via Optimality-Preserving Translation to Average Rewards—0
Sample-Efficient Reinforcement Learning with Temporal Logic Objectives: Leveraging the Task Specification to Guide ExplorationCode0
Off-dynamics Conditional Diffusion Planners—0
Robust RL with LLM-Driven Data Synthesis and Policy Adaptation for Autonomous Driving—0
Bayes Adaptive Monte Carlo Tree Search for Offline Model-based Reinforcement LearningCode0
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Benchmark Results

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