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

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Showing 531–540 of 15113 papers

TitleStatusHype
BQSched: A Non-intrusive Scheduler for Batch Concurrent Queries via Reinforcement LearningCode0
Neurophysiologically Realistic Environment for Comparing Adaptive Deep Brain Stimulation Algorithms in Parkinson DiseaseCode1
Explainable AI for UAV Mobility Management: A Deep Q-Network Approach for Handover Minimization—0
LLM-hRIC: LLM-empowered Hierarchical RAN Intelligent Control for O-RAN—0
Depth-Constrained ASV Navigation with Deep RL and Limited Sensing—0
CaRL: Learning Scalable Planning Policies with Simple RewardsCode2
RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement LearningCode7
Training Large Language Models to Reason via EM Policy Gradient—0
SAPO-RL: Sequential Actuator Placement Optimization for Fuselage Assembly via Reinforcement Learning—0
Integrating Learning-Based Manipulation and Physics-Based Locomotion for Whole-Body Badminton Robot Control—0
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Benchmark Results

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