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 2361–2370 of 15113 papers

TitleStatusHype
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
Consistent Dropout for Policy Gradient Reinforcement Learning—0
Consolidated Adaptive T-soft Update for Deep Reinforcement Learning—0
Constrained Proximal Policy Optimization—0
Constrained Reinforcement Learning via Dissipative Saddle Flow Dynamics—0
Batch Reinforcement Learning with a Nonparametric Off-Policy Policy Gradient—0
Batch Reinforcement Learning with Hyperparameter Gradients—0
An Adaptive Multi-Agent Physical Layer Security Framework for Cognitive Cyber-Physical Systems—0
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Benchmark Results

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