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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2751–2775 of 15113 papers

TitleStatusHype
Imagination-Augmented Hierarchical Reinforcement Learning for Safe and Interactive Autonomous Driving in Urban Environments—0
Data-Driven LQR using Reinforcement Learning and Quadratic Neural Networks—0
Runtime Verification of Learning Properties for Reinforcement Learning Algorithms—0
JaxMARL: Multi-Agent RL Environments and Algorithms in JAXCode2
Reinforcement Learning with Model Predictive Control for Highway Ramp MeteringCode1
On-Policy Policy Gradient Reinforcement Learning Without On-Policy Sampling—0
Adversarial Imitation Learning On Aggregated Data—0
Purpose in the Machine: Do Traffic Simulators Produce Distributionally Equivalent Outcomes for Reinforcement Learning Applications?—0
Direct Preference Optimization for Neural Machine Translation with Minimum Bayes Risk DecodingCode1
Workflow-Guided Response Generation for Task-Oriented Dialogue—0
When Mining Electric Locomotives Meet Reinforcement Learning—0
Combinatorial Optimization with Policy Adaptation using Latent Space SearchCode1
Reinforcement Learning for Solving Stochastic Vehicle Routing ProblemCode0
Investigating Robustness in Cyber-Physical Systems: Specification-Centric Analysis in the face of System Deviations—0
An introduction to reinforcement learning for neuroscience—0
Learning Predictive Safety Filter via Decomposition of Robust Invariant Set—0
An advantage based policy transfer algorithm for reinforcement learning with measures of transferability—0
Genetic Algorithm enhanced by Deep Reinforcement Learning in parent selection mechanism and mutation : Minimizing makespan in permutation flow shop scheduling problems—0
Out-of-Distribution-Aware Electric Vehicle Charging—0
Clipped-Objective Policy Gradients for Pessimistic Policy OptimizationCode0
Accelerating Exploration with Unlabeled Prior DataCode1
From "What" to "When" -- a Spiking Neural Network Predicting Rare Events and Time to their Occurrence—0
LLM Augmented Hierarchical Agents—0
Adaptive Stochastic Nonlinear Model Predictive Control with Look-ahead Deep Reinforcement Learning for Autonomous Vehicle Motion Control—0
Stable Modular Control via Contraction Theory for Reinforcement Learning—0
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Benchmark Results

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