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 3681–3690 of 15113 papers

TitleStatusHype
Graph Reinforcement Learning for Operator Selection in the ALNS Metaheuristic—0
Auxiliary Task-based Deep Reinforcement Learning for Quantum Control—0
Efficient Exploration Using Extra Safety Budget in Constrained Policy Optimization—0
Minimizing the Outage Probability in a Markov Decision Process—0
Exploiting Multiple Abstractions in Episodic RL via Reward ShapingCode0
Multi-Agent Reinforcement Learning for Pragmatic Communication and Control—0
Learning to Control Autonomous Fleets from Observation via Offline Reinforcement LearningCode0
AR3n: A Reinforcement Learning-based Assist-As-Needed Controller for Robotic Rehabilitation—0
Hierarchical Reinforcement Learning in Complex 3D Environments—0
The In-Sample Softmax for Offline Reinforcement LearningCode1
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Benchmark Results

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