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 3971–3980 of 15113 papers

TitleStatusHype
Fast Two-Time-Scale Stochastic Gradient Method with Applications in Reinforcement Learning—0
Deep Reinforcement Learning for Real-Time Ground Delay Program Revision and Corresponding Flight Delay Assignments—0
vMFER: Von Mises-Fisher Experience Resampling Based on Uncertainty of Gradient Directions for Policy Improvement—0
Hype or Heuristic? Quantum Reinforcement Learning for Join Order OptimisationCode0
Neural Network Compression for Reinforcement Learning Tasks—0
Near-Optimal Regret in Linear MDPs with Aggregate Bandit Feedback—0
CAGES: Cost-Aware Gradient Entropy Search for Efficient Local Multi-Fidelity Bayesian OptimizationCode0
Intrinsic Rewards for Exploration without Harm from Observational Noise: A Simulation Study Based on the Free Energy Principle—0
Reducing Risk for Assistive Reinforcement Learning Policies with Diffusion Models—0
Ensemble Successor Representations for Task Generalization in Offline-to-Online Reinforcement Learning—0
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Benchmark Results

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