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 4876–4900 of 15113 papers

TitleStatusHype
Foundations for Restraining Bolts: Reinforcement Learning with LTLf/LDLf restraining specifications—0
Reinforcement Learning for Machine Learning Model Deployment: Evaluating Multi-Armed Bandits in ML Ops Environments—0
Reinforcement Learning for Many-Body Ground-State Preparation Inspired by Counterdiabatic Driving—0
Reinforcement Learning for Markovian Bandits: Is Posterior Sampling more Scalable than Optimism?—0
Reinforcement Learning for Matrix Computations: PageRank as an Example—0
Reinforcement Learning for Mean Field Game—0
Reinforcement Learning for Mean Field Games, with Applications to Economics—0
Distributed Reinforcement Learning for Age of Information Minimization in Real-Time IoT Systems—0
Reinforcement Learning for Mitigating Intermittent Interference in Terahertz Communication Networks—0
Reinforcement Learning for Mixed-Integer Problems Based on MPC—0
Reinforcement Learning for Motor Control: A Comprehensive Review—0
Reinforcement Learning for Multi-Objective Optimization of Online Decisions in High-Dimensional Systems—0
Reinforcement Learning for Multi-Truck Vehicle Routing Problems—0
Reinforcement Learning for Nested Polar Code Construction—0
Reinforcement Learning for Node Selection in Branch-and-Bound—0
Reinforcement learning for non-prehensile manipulation: Transfer from simulation to physical system—0
Reinforcement Learning for Non-Stationary Markov Decision Processes: The Blessing of (More) Optimism—0
Reinforcement Learning for Omega-Regular Specifications on Continuous-Time MDP—0
Reinforcement Learning for on-line Sequence Transformation—0
Reinforcement Learning for Online Testing of Autonomous Driving Systems: a Replication and Extension Study—0
Reinforcement Learning for Optimal Control of a District Cooling Energy Plant—0
Reinforcement Learning for Optimal Load Distribution Sequencing in Resource-Sharing System—0
Reinforcement learning for optimization of variational quantum circuit architectures—0
On-line reinforcement learning for optimization of real-life energy trading strategy—0
Reinforcement Learning for Optimized Beam Training in Multi-Hop Terahertz Communications—0
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Benchmark Results

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