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 9551–9575 of 15113 papers

TitleStatusHype
The Pitfall of More Powerful Autoencoders in Lidar-Based Navigation—0
A deep learning model for gas storage optimization—0
Neural Recursive Belief States in Multi-Agent Reinforcement Learning—0
Multi-UAV Mobile Edge Computing and Path Planning Platform based on Reinforcement Learning—0
Reinforcement Learning with Probabilistic Boolean Network Models of Smart Grid Devices—0
Near-Optimal Offline Reinforcement Learning via Double Variance Reduction—0
A step toward a reinforcement learning de novo genome assembler—0
Metrics and continuity in reinforcement learningCode0
Towards Multi-agent Reinforcement Learning for Wireless Network Protocol Synthesis—0
Approximately Solving Mean Field Games via Entropy-Regularized Deep Reinforcement Learning—0
A Lyapunov Theory for Finite-Sample Guarantees of Asynchronous Q-Learning and TD-Learning Variants—0
An Abstraction-based Method to Check Multi-Agent Deep Reinforcement-Learning Behaviors—0
Improving Reinforcement Learning with Human Assistance: An Argument for Human Subject Studies with HIPPO Gym—0
Hybrid Beamforming for mmWave MU-MISO Systems Exploiting Multi-agent Deep Reinforcement Learning—0
A Secure Learning Control Strategy via Dynamic Camouflaging for Unknown Dynamical Systems under Attacks—0
Bellman Eluder Dimension: New Rich Classes of RL Problems, and Sample-Efficient Algorithms—0
Hybrid Information-driven Multi-agent Reinforcement Learning—0
Interpretable Reinforcement Learning Inspired by Piaget's Theory of Cognitive Development—0
Variation-resistant Q-learning: Controlling and Utilizing Estimation Bias in Reinforcement Learning for Better PerformanceCode0
Risk Aware and Multi-Objective Decision Making with Distributional Monte Carlo Tree Search—0
Throughput Optimization for Grant-Free Multiple Access With Multiagent Deep Reinforcement Learning—0
Fast Rates for the Regret of Offline Reinforcement Learning—0
Improving Human Decision-Making by Discovering Efficient Strategies for Hierarchical Planning—0
Deep Reinforcement Learning-Based Product Recommender for Online Advertising—0
Deep Reinforcement Learning Aided Monte Carlo Tree Search for MIMO Detection—0
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Benchmark Results

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