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 5601–5625 of 15113 papers

TitleStatusHype
Off-Policy Reinforcement Learning with Loss Function Weighted by Temporal Difference Error—0
Novel Reinforcement Learning Algorithm for Suppressing Synchronization in Closed Loop Deep Brain Stimulators—0
Understanding the Complexity Gains of Single-Task RL with a Curriculum—0
Streaming Traffic Flow Prediction Based on Continuous Reinforcement Learning—0
SHIRO: Soft Hierarchical Reinforcement Learning—0
Automated Gadget Discovery in ScienceCode0
Deep Reinforcement Learning for Heat Pump Control—0
Investigation of reinforcement learning for shape optimization of profile extrusion dies—0
NARS vs. Reinforcement learning: ONA vs. Q-LearningCode0
Offline Reinforcement Learning for Human-Guided Human-Machine Interaction with Private Information—0
Reinforcement Learning Based Approaches to Adaptive Context Caching in Distributed Context Management Systems—0
A Learned Simulation Environment to Model Student Engagement and Retention in Automated Online Courses—0
Certified Policy Smoothing for Cooperative Multi-Agent Reinforcement LearningCode0
Decoding surface codes with deep reinforcement learning and probabilistic policy reuse—0
Hyperparameters in Contextual RL are Highly SituationalCode0
Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarios—0
Lifelong Reinforcement Learning with Modulating MasksCode0
A Memetic Algorithm with Reinforcement Learning for Sociotechnical Production Scheduling—0
Control of Continuous Quantum Systems with Many Degrees of Freedom based on Convergent Reinforcement LearningCode0
Robust Path Selection in Software-defined WANs using Deep Reinforcement Learning—0
Neighboring state-based RL Exploration—0
Variational Quantum Soft Actor-Critic for Robotic Arm Control—0
AdverSAR: Adversarial Search and Rescue via Multi-Agent Reinforcement Learning—0
Bandit approach to conflict-free multi-agent Q-learning in view of photonic implementation—0
I Cast Detect Thoughts: Learning to Converse and Guide with Intents and Theory-of-Mind in Dungeons and Dragons—0
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Benchmark Results

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