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 3411–3420 of 15113 papers

TitleStatusHype
Towards Generalizable Reinforcement Learning for Trade Execution—0
Quantile-Based Deep Reinforcement Learning using Two-Timescale Policy Gradient AlgorithmsCode0
On Practical Robust Reinforcement Learning: Practical Uncertainty Set and Double-Agent Algorithm—0
Optimizing Memory Mapping Using Deep Reinforcement Learning—0
Supplementing Gradient-Based Reinforcement Learning with Simple Evolutionary Ideas—0
Discovery of Optimal Quantum Error Correcting Codes via Reinforcement Learning—0
An Option-Dependent Analysis of Regret Minimization Algorithms in Finite-Horizon Semi-Markov Decision Processes—0
Policy Gradient Methods in the Presence of Symmetries and State AbstractionsCode1
Assessment of Reinforcement Learning Algorithms for Nuclear Power Plant Fuel Optimization—0
RLocator: Reinforcement Learning for Bug Localization—0
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Benchmark Results

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