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 9726–9750 of 15113 papers

TitleStatusHype
Capturing Financial markets to apply Deep Reinforcement Learning—0
Deep Reinforcement Learning in Ice Hockey for Context-Aware Player Evaluation—0
Deep Reinforcement Learning in Lane Merge Coordination for Connected Vehicles—0
Deep reinforcement learning in medical imaging: A literature review—0
Deep Reinforcement Learning in mmW-NOMA: Joint Power Allocation and Hybrid Beamforming—0
A View on Deep Reinforcement Learning in System Optimization—0
Deep Reinforcement Learning Microgrid Optimization Strategy Considering Priority Flexible Demand Side—0
Deep Reinforcement Learning Models Predict Visual Responses in the Brain: A Preliminary Result—0
A multi-agent reinforcement learning model of reputation and cooperation in human groups—0
Deep Reinforcement Learning of Cell Movement in the Early Stage of C. elegans Embryogenesis—0
Deep reinforcement learning of event-triggered communication and control for multi-agent cooperative transport—0
Deep Reinforcement Learning of Transition States—0
Deep Reinforcement Learning of Universal Policies with Diverse Environment Summaries—0
Deep reinforcement learning on a multi-asset environment for trading—0
Reinforcement Learning in Practice: Opportunities and Challenges—0
Deep Reinforcement Learning Optimizes Graphene Nanopores for Efficient Desalination—0
Deep Reinforcement Learning Policies Learn Shared Adversarial Features Across MDPs—0
Deep Reinforcement Learning Powered IRS-Assisted Downlink NOMA—0
Deep Reinforcement Learning to Acquire Navigation Skills for Wheel-Legged Robots in Complex Environments—0
Deep reinforcement learning to detect brain lesions on MRI: a proof-of-concept application of reinforcement learning to medical images—0
Deep reinforcement learning under signal temporal logic constraints using Lagrangian relaxation—0
Deep Reinforcement Learning using Capsules in Advanced Game Environments—0
Deep Reinforcement Learning using Cyclical Learning Rates—0
Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Survey—0
Deep Reinforcement Learning with Decorrelation—0
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Benchmark Results

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