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 4151–4160 of 15113 papers

TitleStatusHype
Adapting Surprise Minimizing Reinforcement Learning Techniques for Transactive Control—0
Adapting the Exploration Rate for Value-of-Information-Based Reinforcement Learning—0
Adapting the Function Approximation Architecture in Online Reinforcement Learning—0
Adapting User Interfaces with Model-based Reinforcement Learning—0
Adapting World Models with Latent-State Dynamics Residuals—0
Adaptive 3D UI Placement in Mixed Reality Using Deep Reinforcement Learning—0
Adaptive ABAC Policy Learning: A Reinforcement Learning Approach—0
Adaptive action supervision in reinforcement learning from real-world multi-agent demonstrations—0
Adaptive Actor-Critic Based Optimal Regulation for Drift-Free Uncertain Nonlinear Systems—0
Adaptive Adversarial Training for Meta Reinforcement Learning—0
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Benchmark Results

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