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 3776–3800 of 15113 papers

TitleStatusHype
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
Show:102550
← PrevPage 152 of 605Next →

Benchmark Results

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