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 4541–4550 of 15113 papers

TitleStatusHype
Ambiguous Dynamic Treatment Regimes: A Reinforcement Learning Approach—0
A Memetic Algorithm with Reinforcement Learning for Sociotechnical Production Scheduling—0
A Memory-Based Reinforcement Learning Approach to Integrated Sensing and Communication—0
A Memory Efficient Deep Reinforcement Learning Approach For Snake Game Autonomous Agents—0
Task-Agnostic Learning to Accomplish New Tasks—0
A Meta-Reinforcement Learning Approach to Process Control—0
A Method for Fast Autonomy Transfer in Reinforcement Learning—0
A method for the online construction of the set of states of a Markov Decision Process using Answer Set Programming—0
A Methodology for the Development of RL-Based Adaptive Traffic Signal Controllers—0
A Microscopic Pandemic Simulator for Pandemic Prediction Using Scalable Million-Agent Reinforcement Learning—0
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Benchmark Results

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