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 3491–3500 of 15113 papers

TitleStatusHype
Automated Database Indexing using Model-free Reinforcement Learning—0
DeepRacer: Educational Autonomous Racing Platform for Experimentation with Sim2Real Reinforcement Learning—0
Deep Randomized Least Squares Value Iteration—0
Deep Radial-Basis Value Functions for Continuous Control—0
Accelerating the Computation of UCB and Related Indices for Reinforcement Learning—0
Deep reinforced active learning for multi-class image classification—0
Deep Reinforced Self-Attention Masks for Abstractive Summarization (DR.SAS)—0
Deep Reinforcement Active Learning for Human-in-the-Loop Person Re-Identification—0
Deep Reinforcement Fuzzing—0
Deep RL With Information Constrained Policies: Generalization in Continuous Control—0
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Benchmark Results

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