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 4621–4630 of 15113 papers

TitleStatusHype
An Analysis of Quantile Temporal-Difference Learning—0
An Analysis of Reinforcement Learning for Malaria Control—0
An Analytical Update Rule for General Policy Optimization—0
An application of neural networks to a problem in knot theory and group theory (untangling braids)—0
An application of reinforcement learning to residential energy storage under real-time pricing—0
An approach to implement Reinforcement Learning for Heterogeneous Vehicular Networks—0
An Approach to Partial Observability in Games: Learning to Both Act and Observe—0
A differential Hebbian framework for biologically-plausible motor control—0
An Architecture for Deploying Reinforcement Learning in Industrial Environments—0
An Attempt to Model Human Trust with Reinforcement Learning—0
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Benchmark Results

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