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 2611–2620 of 15113 papers

TitleStatusHype
A Machine Learning Approach for Task and Resource Allocation in Mobile Edge Computing Based Networks—0
A Machine Learning Approach for Prosumer Management in Intraday Electricity Markets—0
Autonomous Maintenance in IoT Networks via AoI-driven Deep Reinforcement Learning—0
Adaptive Multi-pass Decoder for Neural Machine Translation—0
A Cognitive Architecture Based on a Learning Classifier System with Spiking Classifiers—0
Autonomous Learning of Features for Control: Experiments with Embodied and Situated Agents—0
Autonomous Industrial Management via Reinforcement Learning: Self-Learning Agents for Decision-Making -- A Review—0
A Lyapunov Theory for Finite-Sample Guarantees of Asynchronous Q-Learning and TD-Learning Variants—0
Autonomous Highway Driving using Deep Reinforcement Learning—0
Autonomous Extraction of a Hierarchical Structure of Tasks in Reinforcement Learning, A Sequential Associate Rule Mining Approach—0
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Benchmark Results

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