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 2101–2110 of 15113 papers

TitleStatusHype
Intrinsic Rewards for Exploration without Harm from Observational Noise: A Simulation Study Based on the Free Energy Principle—0
Neural Network Compression for Reinforcement Learning Tasks—0
CAGES: Cost-Aware Gradient Entropy Search for Efficient Local Multi-Fidelity Bayesian OptimizationCode0
Hype or Heuristic? Quantum Reinforcement Learning for Join Order OptimisationCode0
Near-Optimal Regret in Linear MDPs with Aggregate Bandit Feedback—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
Fairness in Reinforcement Learning: A Survey—0
Space Processor Computation Time Analysis for Reinforcement Learning and Run Time Assurance Control Policies—0
Improving Targeted Molecule Generation through Language Model Fine-Tuning Via Reinforcement Learning—0
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Benchmark Results

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