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 3751–3800 of 15113 papers

TitleStatusHype
Cost-Aware Dynamic Cloud Workflow Scheduling using Self-Attention and Evolutionary Reinforcement Learning—0
Selftok: Discrete Visual Tokens of Autoregression, by Diffusion, and for Reasoning—0
Deep Reinforcement Learning for Unmanned Aerial Vehicle-Assisted Vehicular Networks—0
Auxiliary Reward Generation with Transition Distance Representation Learning—0
Costate-focused models for reinforcement learning—0
Auxiliary-task Based Deep Reinforcement Learning for Participant Selection Problem in Mobile Crowdsourcing—0
Deep Reinforcement Learning for Visual Object Tracking in Videos—0
Deep Reinforcement Learning for Weapons to Targets Assignment in a Hypersonic strike—0
Deep Decentralized Reinforcement Learning for Cooperative Control—0
Deep Reinforcement Learning for Wireless Resource Allocation Using Buffer State Information—0
A State Aggregation Approach for Solving Knapsack Problem with Deep Reinforcement Learning—0
Deep Reinforcement Learning: Framework, Applications, and Embedded Implementations—0
Accelerating Stochastic Composition Optimization—0
A Variant of the Wang-Foster-Kakade Lower Bound for the Discounted Setting—0
Corruption-Robust Offline Reinforcement Learning—0
Deep Reinforcement Learning from Policy-Dependent Human Feedback—0
Corruption-robust exploration in episodic reinforcement learning—0
A stabilizing reinforcement learning approach for sampled systems with partially unknown models—0
Deep reinforcement learning guided graph neural networks for brain network analysis—0
Automated Video Game Testing Using Synthetic and Human-Like Agents—0
Deep Reinforcement Learning in a Monetary Model—0
Deep Reinforcement Learning in Computer Vision: A Comprehensive Survey—0
Average Cost Optimal Control of Stochastic Systems Using Reinforcement Learning—0
Deep Reinforcement Learning in Cryptocurrency Market Making—0
Discriminator Augmented Model-Based Reinforcement Learning—0
Disentangled Skill Embeddings for Reinforcement Learning—0
Deep Reinforcement Learning in Ice Hockey for Context-Aware Player Evaluation—0
Deep Reinforcement Learning in Lane Merge Coordination for Connected Vehicles—0
Average-Reward Learning and Planning with Options—0
Deep reinforcement learning in medical imaging: A literature review—0
Deep Reinforcement Learning in mmW-NOMA: Joint Power Allocation and Hybrid Beamforming—0
Disentangling Recognition and Decision Regrets in Image-Based Reinforcement Learning—0
Corruption-Robust Algorithms with Uncertainty Weighting for Nonlinear Contextual Bandits and Markov Decision Processes—0
Correlation Priors for Reinforcement Learning—0
Average Reward Reinforcement Learning for Wireless Radio Resource Management—0
A Comparative Analysis of Reinforcement Learning and Conventional Deep Learning Approaches for Bearing Fault Diagnosis—0
Deep Reinforcement Learning Microgrid Optimization Strategy Considering Priority Flexible Demand Side—0
Deep Reinforcement Learning Models Predict Visual Responses in the Brain: A Preliminary Result—0
A multi-agent reinforcement learning model of reputation and cooperation in human groups—0
Deep Reinforcement Learning of Cell Movement in the Early Stage of C. elegans Embryogenesis—0
Deep reinforcement learning of event-triggered communication and control for multi-agent cooperative transport—0
Assured RL: Reinforcement Learning with Almost Sure Constraints—0
Average-Reward Reinforcement Learning with Trust Region Methods—0
A model-based approach to meta-Reinforcement Learning: Transformers and tree search—0
Deep Reinforcement Learning of Transition States—0
Deep Reinforcement Learning of Universal Policies with Diverse Environment Summaries—0
AVID: Learning Multi-Stage Tasks via Pixel-Level Translation of Human Videos—0
Deep reinforcement learning on a multi-asset environment for trading—0
Reinforcement Learning in Practice: Opportunities and Challenges—0
Correlation Filter Selection for Visual Tracking Using Reinforcement Learning—0
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Benchmark Results

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