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 4826–4850 of 15113 papers

TitleStatusHype
DIP-RL: Demonstration-Inferred Preference Learning in Minecraft—0
Game-Theoretic Robust Reinforcement Learning Handles Temporally-Coupled Perturbations—0
Bridging the Reality Gap of Reinforcement Learning based Traffic Signal Control using Domain Randomization and Meta Learning—0
Towards practical reinforcement learning for tokamak magnetic control—0
Reparameterized Policy Learning for Multimodal Trajectory Optimization—0
A reinforcement learning approach for VQA validation: an application to diabetic macular edema grading—0
Distributed 3D-Beam Reforming for Hovering-Tolerant UAVs Communication over Coexistence: A Deep-Q Learning for Intelligent Space-Air-Ground Integrated Networks—0
Continuous-Time Reinforcement Learning: New Design Algorithms with Theoretical Insights and Performance Guarantees—0
Data Cross-Segmentation for Improved Generalization in Reinforcement Learning Based Algorithmic Trading—0
IxDRL: A Novel Explainable Deep Reinforcement Learning Toolkit based on Analyses of InterestingnessCode0
Towards A Unified Agent with Foundation Models—0
REX: Rapid Exploration and eXploitation for AI Agents—0
Quarl: A Learning-Based Quantum Circuit Optimizer—0
Basal-Bolus Advisor for Type 1 Diabetes (T1D) Patients Using Multi-Agent Reinforcement Learning (RL) Methodology—0
An Alternative to Variance: Gini Deviation for Risk-averse Policy Gradient—0
Discovering User Types: Mapping User Traits by Task-Specific Behaviors in Reinforcement Learning—0
POMDP inference and robust solution via deep reinforcement learning: An application to railway optimal maintenanceCode0
Magnetic Field-Based Reward Shaping for Goal-Conditioned Reinforcement Learning—0
Seeing is not Believing: Robust Reinforcement Learning against Spurious Correlation—0
Efficient Action Robust Reinforcement Learning with Probabilistic Policy Execution Uncertainty—0
An Empirical Study of the Effectiveness of Using a Replay Buffer on Mode Discovery in GFlowNets—0
Combining model-predictive control and predictive reinforcement learning for stable quadrupedal robot locomotion—0
Why Guided Dialog Policy Learning performs well? Understanding the role of adversarial learning and its alternative—0
Transformers in Reinforcement Learning: A Survey—0
Learning Decentralized Partially Observable Mean Field Control for Artificial Collective Behavior—0
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Benchmark Results

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