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 3401–3410 of 15113 papers

TitleStatusHype
Deep Reinforcement Learning of Cell Movement in the Early Stage of C. elegans Embryogenesis—0
DeepEdge: A Deep Reinforcement Learning based Task Orchestrator for Edge Computing—0
Deep Reinforcement Learning of Universal Policies with Diverse Environment Summaries—0
Deep Episodic Value Iteration for Model-based Meta-Reinforcement Learning—0
Deep Reinforcement Learning using Cyclical Learning Rates—0
A Kernel-Based Approach to Non-Stationary Reinforcement Learning in Metric Spaces—0
Deep Reinforcement Learning with Iterative Shift for Visual Tracking—0
AceReason-Nemotron: Advancing Math and Code Reasoning through Reinforcement Learning—0
Design for a Darwinian Brain: Part 2. Cognitive Architecture—0
Diffusion-Based Offline RL for Improved Decision-Making in Augmented ARC Task—0
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Benchmark Results

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