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 1511–1520 of 15113 papers

TitleStatusHype
Evening the Score: Targeting SARS-CoV-2 Protease Inhibition in Graph Generative Models for Therapeutic CandidatesCode1
Model-based Multi-agent Policy Optimization with Adaptive Opponent-wise RolloutsCode1
Deep reinforcement learning-designed radiofrequency waveform in MRICode1
Meta-Learning-Based Deep Reinforcement Learning for Multiobjective Optimization ProblemsCode1
Deep Reinforcement Learning for Adaptive Exploration of Unknown EnvironmentsCode1
RL-IoT: Reinforcement Learning to Interact with IoT DevicesCode1
Constructions in combinatorics via neural networksCode1
A Scalable and Reproducible System-on-Chip Simulation for Reinforcement LearningCode1
Computational Performance of Deep Reinforcement Learning to find Nash EquilibriaCode1
Constraint-Guided Reinforcement Learning: Augmenting the Agent-Environment-InteractionCode1
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Benchmark Results

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