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 3981–3990 of 15113 papers

TitleStatusHype
Detecting and adapting to crisis pattern with context based Deep Reinforcement Learning—0
Detecting and Adapting to Novelty in Games—0
Do Deep Reinforcement Learning Algorithms really Learn to Navigate?—0
Detecting Deceptive Reviews using Generative Adversarial Networks—0
Adaptive Stress Testing for Autonomous Vehicles—0
Detecting Worst-case Corruptions via Loss Landscape Curvature in Deep Reinforcement Learning—0
Deterministic Exploration via Stationary Bellman Error Maximization—0
I Cast Detect Thoughts: Learning to Converse and Guide with Intents and Theory-of-Mind in Dungeons and Dragons—0
Bayesian Distributional Policy Gradients—0
Coordinated Reinforcement Learning for Optimizing Mobile Networks—0
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Benchmark Results

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