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 2571–2580 of 15113 papers

TitleStatusHype
Long-term Safe Reinforcement Learning with Binary Feedback—0
Using reinforcement learning to improve drone-based inference of greenhouse gas fluxesCode0
NovelGym: A Flexible Ecosystem for Hybrid Planning and Learning Agents Designed for Open Worlds—0
On Sample-Efficient Offline Reinforcement Learning: Data Diversity, Posterior Sampling, and Beyond—0
Synergistic Formulaic Alpha Generation for Quantitative Trading based on Reinforcement Learning—0
Adaptive Discounting of Training Time Attacks—0
A unified uncertainty-aware exploration: Combining epistemic and aleatory uncertainty—0
A comprehensive survey of research towards AI-enabled unmanned aerial systems in pre-, active-, and post-wildfire management—0
Towards an Adaptable and Generalizable Optimization Engine in Decision and Control: A Meta Reinforcement Learning Approach—0
A Robust Quantile Huber Loss With Interpretable Parameter Adjustment In Distributional Reinforcement LearningCode0
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Benchmark Results

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