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 2691–2700 of 15113 papers

TitleStatusHype
RL-Based Cargo-UAV Trajectory Planning and Cell Association for Minimum Handoffs, Disconnectivity, and Energy Consumption—0
Convergence Rates for Stochastic Approximation: Biased Noise with Unbounded Variance, and Applications—0
Contact Energy Based Hindsight Experience Prioritization—0
MASP: Scalable GNN-based Planning for Multi-Agent Navigation—0
LExCI: A Framework for Reinforcement Learning with Embedded SystemsCode0
Score-Aware Policy-Gradient Methods and Performance Guarantees using Local Lyapunov Conditions: Applications to Product-Form Stochastic Networks and Queueing Systems—0
SPOC: Imitating Shortest Paths in Simulation Enables Effective Navigation and Manipulation in the Real World—0
Adaptive operator selection utilising generalised experience—0
Deep Reinforcement Learning for Community Battery Scheduling under Uncertainties of Load, PV Generation, and Energy Prices—0
Training Reinforcement Learning Agents and Humans With Difficulty-Conditioned Generators—0
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Benchmark Results

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