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 901–910 of 15113 papers

TitleStatusHype
De novo PROTAC design using graph-based deep generative modelsCode1
Learning safety in model-based Reinforcement Learning using MPC and Gaussian ProcessesCode1
Synthesis of separation processes with reinforcement learningCode1
Scalable Multi-Agent Reinforcement Learning through Intelligent Information AggregationCode1
Multi-Agent Reinforcement Learning for Adaptive Mesh RefinementCode1
Causal Counterfactuals for Improving the Robustness of Reinforcement LearningCode1
Spatial-temporal recurrent reinforcement learning for autonomous shipsCode1
Online Control of Adaptive Large Neighborhood Search using Deep Reinforcement LearningCode1
Agent-Controller Representations: Principled Offline RL with Rich Exogenous InformationCode1
RLET: A Reinforcement Learning Based Approach for Explainable QA with Entailment TreesCode1
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Benchmark Results

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