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 3501–3525 of 15113 papers

TitleStatusHype
Deep Reinforcement Learning, a textbook—0
Deep Reinforcement Learning Aided Monte Carlo Tree Search for MIMO Detection—0
Deep Reinforcement Learning Aided Platoon Control Relying on V2X Information—0
Deep Reinforcement Learning-Aided RAN Slicing Enforcement for B5G Latency Sensitive Services—0
Automated Hybrid Reward Scheduling via Large Language Models for Robotic Skill Learning—0
Deep Reinforcement Learning amidst Lifelong Non-Stationarity—0
Deep Reinforcement Learning and Transportation Research: A Comprehensive Review—0
Deep Reinforcement Learning and Convex Mean-Variance Optimisation for Portfolio Management—0
Deep Reinforcement Learning and its Neuroscientific Implications—0
Deep Reinforcement Learning and Permissioned Blockchain for Content Caching in Vehicular Edge Computing and Networks—0
Deep Reinforcement Learning and the Deadly Triad—0
Automated Lane Change Strategy using Proximal Policy Optimization-based Deep Reinforcement Learning—0
Deep Reinforcement Learning: An Overview—0
Deep Reinforcement Learning Approach for Trading Automation in The Stock Market—0
Deep reinforcement learning approach to MIMO precoding problem: Optimality and Robustness—0
Deep Reinforcement Learning Assisted Federated Learning Algorithm for Data Management of IIoT—0
Deep Reinforcement Learning Attention Selection for Person Re-Identification—0
Agent based modelling for continuously varying supply chains—0
Accelerating the Computation of UCB and Related Indices for Reinforcement Learning—0
Defense Against Reward Poisoning Attacks in Reinforcement Learning—0
Deep Reinforcement Learning Based Multidimensional Resource Management for Energy Harvesting Cognitive NOMA Communications—0
Adversarial Deep Reinforcement Learning based Adaptive Moving Target Defense—0
Deep Reinforcement Learning-Based Adaptive IRS Control with Limited Feedback Codebooks—0
Deep Reinforcement Learning-based Anti-jamming Power Allocation in a Two-cell NOMA Network—0
Deflated Dynamics Value Iteration—0
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Benchmark Results

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