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 4676–4700 of 15113 papers

TitleStatusHype
Reinforcement Learning Approach for Multi-Agent Flexible Scheduling Problems—0
Multi-agent Deep Covering Skill Discovery—0
BAFFLE: Hiding Backdoors in Offline Reinforcement Learning DatasetsCode1
How to Enable Uncertainty Estimation in Proximal Policy Optimization—0
Advice Conformance Verification by Reinforcement Learning agents for Human-in-the-Loop—0
Algorithmic Trading Using Continuous Action Space Deep Reinforcement Learning—0
Low-Thrust Orbital Transfer using Dynamics-Agnostic Reinforcement Learning—0
Digital Human Interactive Recommendation Decision-Making Based on Reinforcement Learning—0
Deep Inventory Management—0
Exploration via Planning for Information about the Optimal TrajectoryCode1
Neuroevolution is a Competitive Alternative to Reinforcement Learning for Skill DiscoveryCode1
Lyapunov Function Consistent Adaptive Network Signal Control with Back Pressure and Reinforcement Learning—0
Rainier: Reinforced Knowledge Introspector for Commonsense Question AnsweringCode1
Meta Reinforcement Learning for Optimal Design of Legged Robots—0
Reinforcement Learning with Large Action Spaces for Neural Machine Translation—0
Learning Algorithms for Intelligent Agents and Mechanisms—0
Deep Reinforcement Learning based Evasion Generative Adversarial Network for Botnet DetectionCode1
Distributionally Adaptive Meta Reinforcement Learning—0
Discovering faster matrix multiplication algorithms with reinforcement learningCode4
A Novel Entropy-Maximizing TD3-based Reinforcement Learning for Automatic PID Tuning—0
Option-Aware Adversarial Inverse Reinforcement Learning for Robotic ControlCode1
DreamShard: Generalizable Embedding Table Placement for Recommender SystemsCode1
Query The Agent: Improving sample efficiency through epistemic uncertainty estimation—0
Towards Safe Mechanical Ventilation Treatment Using Deep Offline Reinforcement LearningCode0
Real-Time Reinforcement Learning for Vision-Based Robotics Utilizing Local and Remote ComputersCode1
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Benchmark Results

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