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 11451–11500 of 15113 papers

TitleStatusHype
Visual Tracking by means of Deep Reinforcement Learning and an Expert Demonstrator—0
Visuomotor Mechanical Search: Learning to Retrieve Target Objects in Clutter—0
ViVa: Video-Trained Value Functions for Guiding Online RL from Diverse Data—0
Vizarel: A System to Help Better Understand RL Agents—0
VLMLight: Traffic Signal Control via Vision-Language Meta-Control and Dual-Branch Reasoning—0
VLM Q-Learning: Aligning Vision-Language Models for Interactive Decision-Making—0
VLM-RL: A Unified Vision Language Models and Reinforcement Learning Framework for Safe Autonomous Driving—0
VLP: Vision-Language Preference Learning for Embodied Manipulation—0
VL-SAFE: Vision-Language Guided Safety-Aware Reinforcement Learning with World Models for Autonomous Driving—0
VMAV-C: A Deep Attention-based Reinforcement Learning Algorithm for Model-based Control—0
vMFER: Von Mises-Fisher Experience Resampling Based on Uncertainty of Gradient Directions for Policy Improvement—0
VolleyBots: A Testbed for Multi-Drone Volleyball Game Combining Motion Control and Strategic Play—0
VOQL: Towards Optimal Regret in Model-free RL with Nonlinear Function Approximation—0
Voting-Based Multi-Agent Reinforcement Learning for Intelligent IoT—0
VPE: Variational Policy Embedding for Transfer Reinforcement Learning—0
VRAIL: Vectorized Reward-based Attribution for Interpretable Learning—0
VRLS: A Unified Reinforcement Learning Scheduler for Vehicle-to-Vehicle Communications—0
Advancing Autonomous VLM Agents via Variational Subgoal-Conditioned Reinforcement Learning—0
Vulcan: Solving the Steiner Tree Problem with Graph Neural Networks and Deep Reinforcement Learning—0
Vulnerability-Aware Poisoning Mechanism for Online RL with Unknown Dynamics—0
WAD: A Deep Reinforcement Learning Agent for Urban Autonomous Driving—0
Wall Street Tree Search: Risk-Aware Planning for Offline Reinforcement Learning—0
Warm-Start Actor-Critic: From Approximation Error to Sub-optimality Gap—0
Warmth and competence in human-agent cooperation—0
Warm-up Free Policy Optimization: Improved Regret in Linear Markov Decision Processes—0
Warren at SemEval-2020 Task 4: ALBERT and Multi-Task Learning for Commonsense Validation—0
Wasserstein Actor-Critic: Directed Exploration via Optimism for Continuous-Actions Control—0
Wasserstein Adversarial Imitation Learning—0
Wasserstein Dependency Measure for Representation Learning—0
Wasserstein Robust Reinforcement Learning—0
Wasserstein Unsupervised Reinforcement Learning—0
Watch from sky: machine-learning-based multi-UAV network for predictive police surveillance—0
Stop-and-Go: Exploring Backdoor Attacks on Deep Reinforcement Learning-based Traffic Congestion Control Systems—0
WaveCorr: Deep Reinforcement Learning with Permutation Invariant Policy Networks for Portfolio Management—0
Way Off-Policy Batch Deep Reinforcement Learning of Implicit Human Preferences in Dialog—0
Way Off-Policy Batch Deep Reinforcement Learning of Human Preferences in Dialog—0
On L_2-consistency of nearest neighbor matching—0
Weakly Supervised Disentangled Representation for Goal-conditioned Reinforcement Learning—0
Weakly-Supervised Learning of Disentangled and Interpretable Skills for Hierarchical Reinforcement Learning—0
Weakly-Supervised Reinforcement Learning for Controllable Behavior—0
Weakly Supervised Video Summarization by Hierarchical Reinforcement Learning—0
Weakness Analysis of Cyberspace Configuration Based on Reinforcement Learning—0
Weber-Fechner Law in Temporal Difference learning derived from Control as Inference—0
WebWISE: Web Interface Control and Sequential Exploration with Large Language Models—0
Weighted Bellman Backups for Improved Signal-to-Noise in Q-Updates—0
Weighted Double Deep Multiagent Reinforcement Learning in Stochastic Cooperative Environments—0
Weighted Entropy Modification for Soft Actor-Critic—0
Weighted Likelihood Policy Search with Model Selection—0
Weighted Maximum Entropy Inverse Reinforcement Learning—0
Weighted model estimation for offline model-based reinforcement learning—0
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Benchmark Results

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