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 10851–10875 of 15113 papers

TitleStatusHype
Zeroth-Order Supervised Policy Improvement—0
Surveys without Questions: A Reinforcement Learning Approach—0
Sample Efficient Reinforcement Learning via Low-Rank Matrix Estimation—0
Multi-Agent Informational Learning Processes—0
Scalable Multi-Agent Reinforcement Learning for Networked Systems with Average Reward—0
Off-Policy Risk-Sensitive Reinforcement Learning Based Constrained Robust Optimal Control—0
Q-greedyUCB: a New Exploration Policy for Adaptive and Resource-efficient Scheduling—0
Continuous Action Reinforcement Learning from a Mixture of Interpretable ExpertsCode0
Machine learning and control engineering: The model-free case—0
Searching Learning Strategy with Reinforcement Learning for 3D Medical Image Segmentation—0
Privacy-Cost Management in Smart Meters with Mutual Information-Based Reinforcement Learning—0
Multi-Agent Reinforcement Learning in a Realistic Limit Order Book Market Simulation—0
Transient Non-Stationarity and Generalisation in Deep Reinforcement Learning—0
Self-Supervised Reinforcement Learning for Recommender Systems—0
Deep reinforcement learning for optical systems: A case study of mode-locked lasers—0
Learning to Play Table Tennis From Scratch using Muscular Robots—0
Development of A Stochastic Traffic Environment with Generative Time-Series Models for Improving Generalization Capabilities of Autonomous Driving Agents—0
Causal Discovery from Incomplete Data using An Encoder and Reinforcement Learning—0
An overall view of key problems in algorithmic trading and recent progress—0
Distributed Learning on Heterogeneous Resource-Constrained Devices—0
Stealing Deep Reinforcement Learning Models for Fun and Profit—0
Policy-focused Agent-based Modeling using RL Behavioral Models—0
Online Learning in Iterated Prisoner's Dilemma to Mimic Human BehaviorCode0
Variational Model-based Policy Optimization—0
Online Data Poisoning Attacks—0
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Benchmark Results

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