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 9776–9800 of 15113 papers

TitleStatusHype
Deep Reinforcement Learning with Surrogate Agent-Environment Interface—0
Deep Reinforcement Learning with Symmetric Prior for Predictive Power Allocation to Mobile Users—0
Deep reinforcement learning with symmetric data augmentation applied for aircraft lateral attitude tracking control—0
Deep Reinforcement Learning with Vector Quantized Encoding—0
Deep Reinforcement Learning with Weighted Q-Learning—0
Deep Residual Reinforcement Learning—0
Deep RL-based Trajectory Planning for AoI Minimization in UAV-assisted IoT—0
Deep RL for Blood Glucose Control: Lessons, Challenges, and Opportunities—0
Deep RL with Hierarchical Action Exploration for Dialogue Generation—0
Deep RL With Information Constrained Policies: Generalization in Continuous Control—0
DeepRNG: Towards Deep Reinforcement Learning-Assisted Generative Testing of Software—0
DeepScalper: A Risk-Aware Reinforcement Learning Framework to Capture Fleeting Intraday Trading Opportunities—0
Deep Sets for Generalization in RL—0
Deep SIMBAD: Active Landmark-based Self-localization Using Ranking -based Scene Descriptor—0
DeepSlicing: Deep Reinforcement Learning Assisted Resource Allocation for Network Slicing—0
Deep Surrogate Assisted Generation of Environments—0
DeepThermal: Combustion Optimization for Thermal Power Generating Units Using Offline Reinforcement Learning—0
Deep Transfer Q-Learning for Offline Non-Stationary Reinforcement Learning—0
DeepVideo-R1: Video Reinforcement Fine-Tuning via Difficulty-aware Regressive GRPO—0
Deep VULMAN: A Deep Reinforcement Learning-Enabled Cyber Vulnerability Management Framework—0
DeepWiVe: Deep-Learning-Aided Wireless Video Transmission—0
DEER: A Delay-Resilient Framework for Reinforcement Learning with Variable Delays—0
Adversary Agnostic Robust Deep Reinforcement Learning—0
Defense Against Reward Poisoning Attacks in Reinforcement Learning—0
Defensive Escort Teams via Multi-Agent Deep Reinforcement Learning—0
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Benchmark Results

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