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 6026–6050 of 15113 papers

TitleStatusHype
Skill Transfer in Deep Reinforcement Learning under Morphological Heterogeneity—0
Skip Training for Multi-Agent Reinforcement Learning Controller for Industrial Wave Energy Converters—0
skrl: Modular and Flexible Library for Reinforcement Learning—0
SLAC: Simulation-Pretrained Latent Action Space for Whole-Body Real-World RL—0
SlateFree: a Model-Free Decomposition for Reinforcement Learning with Slate Actions—0
SleeperNets: Universal Backdoor Poisoning Attacks Against Reinforcement Learning Agents—0
SLiM-Gym: Reinforcement Learning for Population Genetics—0
Slowly Changing Adversarial Bandit Algorithms are Efficient for Discounted MDPs—0
Smaller Batches, Bigger Gains? Investigating the Impact of Batch Sizes on Reinforcement Learning Based Real-World Production Scheduling—0
Smart caching in a Data Lake for High Energy Physics analysis—0
SmartChoices: Hybridizing Programming and Machine Learning—0
Smart Exploration in Reinforcement Learning using Bounded Uncertainty Models—0
Smart Feasibility Pump: Reinforcement Learning for (Mixed) Integer Programming—0
Smart Interference Management xApp using Deep Reinforcement Learning—0
SmartPathfinder: Pushing the Limits of Heuristic Solutions for Vehicle Routing Problem with Drones Using Reinforcement Learning—0
Smart Scheduling based on Deep Reinforcement Learning for Cellular Networks—0
SMART: Self-supervised Multi-task pretrAining with contRol Transformers—0
Smart Train Operation Algorithms based on Expert Knowledge and Reinforcement Learning—0
Smell of Source: Learning-Based Odor Source Localization with Molecular Communication—0
Smoothed Action Value Functions for Learning Gaussian Policies—0
Smoothed functional-based gradient algorithms for off-policy reinforcement learning: A non-asymptotic viewpoint—0
Smoothed Q-learning—0
Smooth Imitation Learning via Smooth Costs and Smooth Policies—0
Smoothing Deep Reinforcement Learning for Power Control for Spectrum Sharing in Cognitive Radios—0
Smooth Trajectory Collision Avoidance through Deep Reinforcement Learning—0
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Benchmark Results

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