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 8851–8875 of 15113 papers

TitleStatusHype
Experience-Based Heuristic Search: Robust Motion Planning with Deep Q-Learning—0
Deceptive Reinforcement Learning for Privacy-Preserving Planning—0
Finite Sample Analysis of Minimax Offline Reinforcement Learning: Completeness, Fast Rates and First-Order Efficiency—0
Deep reinforcement learning for smart calibration of radio telescopesCode0
Revisiting Prioritized Experience Replay: A Value PerspectiveCode0
Provably Efficient Algorithms for Multi-Objective Competitive RL—0
Persistent Rule-based Interactive Reinforcement Learning—0
A review of motion planning algorithms for intelligent robotics—0
Deep reinforcement learning-based image classification achieves perfect testing set accuracy for MRI brain tumors with a training set of only 30 images—0
Hybrid Adversarial Imitation Learning—0
Alchemy: A benchmark and analysis toolkit for meta-reinforcement learning agentsCode1
How to Train Your Robot with Deep Reinforcement Learning; Lessons We've Learned—0
A deep learning model for gas storage optimization—0
The Pitfall of More Powerful Autoencoders in Lidar-Based Navigation—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
Near-Optimal Offline Reinforcement Learning via Double Variance Reduction—0
A step toward a reinforcement learning de novo genome assembler—0
Improving Reinforcement Learning with Human Assistance: An Argument for Human Subject Studies with HIPPO Gym—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
Approximately Solving Mean Field Games via Entropy-Regularized Deep Reinforcement Learning—0
Metrics and continuity in reinforcement learningCode0
Towards Multi-agent Reinforcement Learning for Wireless Network Protocol Synthesis—0
Reinforcement Learning with Probabilistic Boolean Network Models of Smart Grid Devices—0
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Benchmark Results

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