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 9526–9550 of 15113 papers

TitleStatusHype
Unlocking Pixels for Reinforcement Learning via Implicit Attention—0
Neurogenetic Programming Framework for Explainable Reinforcement LearningCode0
Generate and Revise: Reinforcement Learning in Neural Poetry—0
Contrasting Centralized and Decentralized Critics in Multi-Agent Reinforcement Learning—0
Introduction to Machine Learning for the Sciences—0
Learning Optimal Strategies for Temporal Tasks in Stochastic Games—0
An Analysis of Frame-skipping in Reinforcement Learning—0
Sparsely ensembled convolutional neural network classifiers via reinforcement learningCode0
Multi-Agent Deep Reinforcement Learning for Request Dispatching in Distributed-Controller Software-Defined Networking—0
A bandit approach to curriculum generation for automatic speech recognition—0
Improving Model and Search for Computer Go—0
MSPM: A Modularized and Scalable Multi-Agent Reinforcement Learning-based System for Financial Portfolio Management—0
A Hybrid Approach for Reinforcement Learning Using Virtual Policy Gradient for Balancing an Inverted Pendulum—0
Finite Sample Analysis of Minimax Offline Reinforcement Learning: Completeness, Fast Rates and First-Order Efficiency—0
Experience-Based Heuristic Search: Robust Motion Planning with Deep Q-Learning—0
Deceptive Reinforcement Learning for Privacy-Preserving Planning—0
Addressing Inherent Uncertainty: Risk-Sensitive Behavior Generation for Automated Driving using Distributional Reinforcement Learning—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
Hybrid Adversarial Imitation 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
How to Train Your Robot with Deep Reinforcement Learning; Lessons We've Learned—0
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Benchmark Results

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