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 3851–3900 of 15113 papers

TitleStatusHype
Distributional Reinforcement Learning for Efficient Exploration—0
Deep Reinforcement Learning with Vector Quantized Encoding—0
Corruption-Robust Algorithms with Uncertainty Weighting for Nonlinear Contextual Bandits and Markov Decision Processes—0
Correlation Priors for Reinforcement Learning—0
Assured RL: Reinforcement Learning with Almost Sure Constraints—0
Correlation Filter Selection for Visual Tracking Using Reinforcement Learning—0
Assured Learning-enabled Autonomy: A Metacognitive Reinforcement Learning Framework—0
ActSafe: Active Exploration with Safety Constraints for 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
Accelerating Self-Imitation Learning from Demonstrations via Policy Constraints and Q-Ensemble—0
Assume-Guarantee Reinforcement Learning—0
DeepScalper: A Risk-Aware Reinforcement Learning Framework to Capture Fleeting Intraday Trading Opportunities—0
Distributed Reinforcement Learning of Targeted Grasping with Active Vision for Mobile Manipulators—0
Correct-by-synthesis reinforcement learning with temporal logic constraints—0
Associative Memory Based Experience Replay for Deep Reinforcement Learning—0
A Multi-Agent Deep Reinforcement Learning Approach for a Distributed Energy Marketplace in Smart Grids—0
Deep SIMBAD: Active Landmark-based Self-localization Using Ranking -based Scene Descriptor—0
A Generative Framework for Simultaneous Machine Translation—0
BADDr: Bayes-Adaptive Deep Dropout RL for POMDPs—0
BadGPT: Exploring Security Vulnerabilities of ChatGPT via Backdoor Attacks to InstructGPT—0
Deep Surrogate Assisted Generation of Environments—0
Distributed Reinforcement Learning via Gossip—0
A General Theory of Relativity in Reinforcement Learning—0
Adaptive Road Configurations for Improved Autonomous Vehicle-Pedestrian Interactions using Reinforcement Learning—0
DeepThermal: Combustion Optimization for Thermal Power Generating Units Using Offline Reinforcement Learning—0
CORE: Constraint-Aware One-Step Reinforcement Learning for Simulation-Guided Neural Network Accelerator Design—0
Effective Reinforcement Learning Based on Structural Information Principles—0
Deep Transfer Q-Learning for Offline Non-Stationary Reinforcement Learning—0
A Comparative Study of Reinforcement Learning Techniques on Dialogue Management—0
CORAL: Contextual Response Retrievability Loss Function for Training Dialog Generation Models—0
A Multiagent Reinforcement Learning Algorithm with Non-linear Dynamics—0
ACTRCE: Augmenting Experience via Teacher’s Advice—0
DeepVideo-R1: Video Reinforcement Fine-Tuning via Difficulty-aware Regressive GRPO—0
Balancing a CartPole System with Reinforcement Learning -- A Tutorial—0
Deep VULMAN: A Deep Reinforcement Learning-Enabled Cyber Vulnerability Management Framework—0
DeepWiVe: Deep-Learning-Aided Wireless Video Transmission—0
Balancing Accuracy and Fairness for Interactive Recommendation with Reinforcement Learning—0
CoRAL: Collaborative Retrieval-Augmented Large Language Models Improve Long-tail Recommendation—0
Adversary Agnostic Robust Deep Reinforcement Learning—0
Balancing Constraints and Rewards with Meta-Gradient D4PG—0
Defense Against Reward Poisoning Attacks in Reinforcement Learning—0
Assessment of Reward Functions in Reinforcement Learning for Multi-Modal Urban Traffic Control under Real-World limitations—0
Defining Admissible Rewards for High Confidence Policy Evaluation—0
Definition and evaluation of model-free coordination of electrical vehicle charging with reinforcement learning—0
A General Perspective on Objectives of Reinforcement Learning—0
Deflated Dynamics Value Iteration—0
Distributed Reinforcement Learning for Robot Teams: A Review—0
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Benchmark Results

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