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 4176–4200 of 15113 papers

TitleStatusHype
Adaptive Discretization in Online Reinforcement Learning—0
Adaptive Droplet Routing in Digital Microfluidic Biochips Using Deep Reinforcement Learning—0
Adaptive Energy Management for Real Driving Conditions via Transfer Reinforcement Learning—0
Adaptive Experience Selection for Policy Gradient—0
Adaptive Federated Learning and Digital Twin for Industrial Internet of Things—0
Adaptive Genomic Evolution of Neural Network Topologies (AGENT) for State-to-Action Mapping in Autonomous Agents—0
Adaptive Graph Capsule Convolutional Networks—0
Adaptive Height Optimisation for Cellular-Connected UAVs using Reinforcement Learning—0
Adaptive Honeypot Engagement through Reinforcement Learning of Semi-Markov Decision Processes—0
Adaptive Informative Path Planning Using Deep Reinforcement Learning for UAV-based Active Sensing—0
Adaptive Insurance Reserving with CVaR-Constrained Reinforcement Learning under Macroeconomic Regimes—0
Adaptive Intelligent Secondary Control of Microgrids Using a Biologically-Inspired Reinforcement Learning—0
Adaptive learning for financial markets mixing model-based and model-free RL for volatility targeting—0
Adaptive Learning of Design Strategies over Non-Hierarchical Multi-Fidelity Models via Policy Alignment—0
Adaptive Learning Rates for Multi-Agent Reinforcement Learning—0
Adaptive Load Shedding for Grid Emergency Control via Deep Reinforcement Learning—0
Adaptive model selection in photonic reservoir computing by reinforcement learning—0
Adaptive Modulation and Coding based on Reinforcement Learning for 5G Networks—0
Adaptive Multi-Fidelity Reinforcement Learning for Variance Reduction in Engineering Design Optimization—0
Adaptive Multi-model Fusion Learning for Sparse-Reward Reinforcement Learning—0
Adaptive Multi-pass Decoder for Neural Machine Translation—0
Adaptive Neural Architectures for Recommender Systems—0
Adaptive operator selection utilising generalised experience—0
Adaptive optimal training of animal behavior—0
Adaptive Parameter Selection in Evolutionary Algorithms by Reinforcement Learning with Dynamic Discretization of Parameter Range—0
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Benchmark Results

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