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 5726–5750 of 15113 papers

TitleStatusHype
Characterizing the Action-Generalization Gap in Deep Q-Learning—0
Developing cooperative policies for multi-stage reinforcement learning tasks—0
VesNet-RL: Simulation-based Reinforcement Learning for Real-World US Probe NavigationCode1
State Encoders in Reinforcement Learning for Recommendation: A Reproducibility StudyCode1
On the Verge of Solving Rocket League using Deep Reinforcement Learning and Sim-to-sim Transfer—0
Hybrid Reinforcement Learning for STAR-RISs: A Coupled Phase-Shift Model Based Beamformer—0
Efficient Risk-Averse Reinforcement LearningCode1
Gamma and Vega Hedging Using Deep Distributional Reinforcement LearningCode1
Accelerated Reinforcement Learning for Temporal Logic Control Objectives—0
DxFormer: A Decoupled Automatic Diagnostic System Based on Decoder-Encoder Transformer with Dense Symptom RepresentationsCode1
Learning to Brachiate via Simplified Model ImitationCode1
Introduction to Soar—0
Simultaneous Double Q-learning with Conservative Advantage Learning for Actor-Critic MethodsCode0
Applications of Reinforcement Learning in Deregulated Power Market: A Comprehensive Review—0
Deep Reinforcement Learning-Based Adaptive IRS Control with Limited Feedback Codebooks—0
Search-Based Testing of Reinforcement Learning—0
JUNO: Jump-Start Reinforcement Learning-based Node Selection for UWB Indoor Localization—0
Dynamically writing coupled memories using a reinforcement learning agent, meeting physical bounds—0
A Deep Reinforcement Learning-based Sliding Mode Control Design for Partially-known Nonlinear Systems—0
Reinforcement Learning Approach to Estimation in Linear Systems—0
Goal-Oriented Next Best Activity Recommendation using Reinforcement Learning—0
Alternating Good-for-MDP Automata—0
How to Spend Your Robot Time: Bridging Kickstarting and Offline Reinforcement Learning for Vision-based Robotic Manipulation—0
Generative methods for sampling transition paths in molecular dynamics—0
LDSA: Learning Dynamic Subtask Assignment in Cooperative Multi-Agent Reinforcement Learning—0
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Benchmark Results

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