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 2351–2360 of 15113 papers

TitleStatusHype
Bayesian Bellman Operators—0
An agent-driven semantical identifier using radial basis neural networks and reinforcement learning—0
An Affective Robot Companion for Assisting the Elderly in a Cognitive Game Scenario—0
Adaptive Stress Testing for Adversarial Learning in a Financial Environment—0
Battery Model Calibration with Deep Reinforcement Learning—0
An advantage based policy transfer algorithm for reinforcement learning with measures of transferability—0
BATS: Best Action Trajectory Stitching—0
An adaptive synchronization approach for weights of deep reinforcement learning—0
Adaptive Stress Testing: Finding Likely Failure Events with Reinforcement Learning—0
Conservative Data Sharing for Multi-Task Offline Reinforcement Learning—0
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Benchmark Results

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