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 2551–2560 of 15113 papers

TitleStatusHype
Auxiliary-task Based Deep Reinforcement Learning for Participant Selection Problem in Mobile Crowdsourcing—0
A Microscopic Pandemic Simulator for Pandemic Prediction Using Scalable Million-Agent Reinforcement Learning—0
Auxiliary Reward Generation with Transition Distance Representation Learning—0
Autoverse: An Evolvable Game Language for Learning Robust Embodied Agents—0
A Methodology for the Development of RL-Based Adaptive Traffic Signal Controllers—0
Adaptive Policy Learning for Offline-to-Online Reinforcement Learning—0
Autotuning PID control using Actor-Critic Deep Reinforcement Learning—0
Auto-tuning Distributed Stream Processing Systems using Reinforcement Learning—0
A method for the online construction of the set of states of a Markov Decision Process using Answer Set Programming—0
Autotelic Reinforcement Learning: Exploring Intrinsic Motivations for Skill Acquisition in Open-Ended Environments—0
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Benchmark Results

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