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 1781–1790 of 15113 papers

TitleStatusHype
A Traffic Light Dynamic Control Algorithm with Deep Reinforcement Learning Based on GNN PredictionCode1
Neurosymbolic Reinforcement Learning with Formally Verified ExplorationCode1
Symbolic Relational Deep Reinforcement Learning based on Graph Neural Networks and Autoregressive Policy DecompositionCode1
Continual Model-Based Reinforcement Learning with HypernetworksCode1
CertRL: Formalizing Convergence Proofs for Value and Policy Iteration in CoqCode1
Deep Reinforcement Learning for Process SynthesisCode1
RL STaR Platform: Reinforcement Learning for Simulation based Training of RobotsCode1
SREC: Proactive Self-Remedy of Energy-Constrained UAV-Based Networks via Deep Reinforcement LearningCode1
Competitiveness of MAP-Elites against Proximal Policy Optimization on locomotion tasks in deterministic simulationsCode1
Finding Effective Security Strategies through Reinforcement Learning and Self-PlayCode1
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Benchmark Results

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