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 3961–3970 of 15113 papers

TitleStatusHype
Designing a Prospective COVID-19 Therapeutic with Reinforcement Learning—0
Designing Biological Sequences via Meta-Reinforcement Learning and Bayesian Optimization—0
Designing Composites with Target Effective Young's Modulus using Reinforcement Learning—0
Designing Deep Reinforcement Learning for Human Parameter Exploration—0
Designing high-fidelity multi-qubit gates for semiconductor quantum dots through deep reinforcement learning—0
Designing Interpretable Approximations to Deep Reinforcement Learning—0
Coordinating Policies Among Multiple Agents via an Intelligent Communication Channel—0
Coordinating Disaster Emergency Response with Heuristic Reinforcement Learning—0
Designing realistic RL environment for power systems—0
A Generalized Projected Bellman Error for Off-policy Value Estimation in Reinforcement Learning—0
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Benchmark Results

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