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 9351–9375 of 15113 papers

TitleStatusHype
A Reinforcement Learning Based R-Tree for Spatial Data Indexing in Dynamic Environments—0
Increasing Energy Efficiency of Massive-MIMO Network via Base Stations Switching using Reinforcement Learning and Radio Environment Maps—0
Adversarial Reinforcement Learning for Procedural Content Generation—0
An Energy-Saving Snake Locomotion Gait Policy Obtained Using Deep Reinforcement Learning—0
Distributed Reinforcement Learning for Flexible and Efficient UAV Swarm Control—0
A multi-agent reinforcement learning model of reputation and cooperation in human groups—0
A Taxonomy of Similarity Metrics for Markov Decision Processes—0
Instabilities of Offline RL with Pre-Trained Neural Representation—0
Learning Human Rewards by Inferring Their Latent Intelligence Levels in Multi-Agent Games: A Theory-of-Mind Approach with Application to Driving Data—0
A Lower Bound for the Sample Complexity of Inverse Reinforcement Learning—0
Markov Cricket: Using Forward and Inverse Reinforcement Learning to Model, Predict And Optimize Batting Performance in One-Day International Cricket—0
Passing Through Narrow Gaps with Deep Reinforcement Learning—0
Visual Explanation using Attention Mechanism in Actor-Critic-based Deep Reinforcement Learning—0
Reinforcement Learning, Bit by Bit—0
SAINT-ACC: Safety-Aware Intelligent Adaptive Cruise Control for Autonomous Vehicles Using Deep Reinforcement Learning—0
Asymptotic Theory for IV-Based Reinforcement Learning with Potential Endogeneity—0
Automatic Exploration Process Adjustment for Safe Reinforcement Learning with Joint Chance Constraint Satisfaction—0
Deep reinforcement learning in medical imaging: A literature review—0
A Dual-Memory Architecture for Reinforcement Learning on Neuromorphic Platforms—0
Two-step reinforcement learning for model-free redesign of nonlinear optimal regulatorCode0
Routing algorithms as tools for integrating social distancing with emergency evacuation—0
Unsupervised Learning for Robust Fitting:A Reinforcement Learning ApproachCode0
Neuromechanics-based Deep Reinforcement Learning of Neurostimulation Control in FES cycling—0
An RL-Based Adaptive Detection Strategy to Secure Cyber-Physical Systems—0
Conservative Optimistic Policy Optimization via Multiple Importance SamplingCode0
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Benchmark Results

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