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 2341–2350 of 15113 papers

TitleStatusHype
Bayesian Exploration Networks—0
Bayesian Exploration for Lifelong Reinforcement Learning—0
An Algorithmic Theory of Metacognition in Minds and Machines—0
Adaptive Stress Testing without Domain Heuristics using Go-Explore—0
Bayesian Distributional Policy Gradients—0
I Cast Detect Thoughts: Learning to Converse and Guide with Intents and Theory-of-Mind in Dungeons and Dragons—0
Monte Carlo Bayesian Reinforcement Learning—0
Adaptive Stress Testing for Autonomous Vehicles—0
Bayesian Critique-Tune-Based Reinforcement Learning with Adaptive Pressure for Multi-Intersection Traffic Signal Control—0
A Comparison of Reinforcement Learning Techniques for Fuzzy Cloud Auto-Scaling—0
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Benchmark Results

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