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 8876–8900 of 15113 papers

TitleStatusHype
Throughput Optimization for Grant-Free Multiple Access With Multiagent Deep Reinforcement Learning—0
Variation-resistant Q-learning: Controlling and Utilizing Estimation Bias in Reinforcement Learning for Better PerformanceCode0
NeoRL: A Near Real-World Benchmark for Offline Reinforcement LearningCode1
Risk Aware and Multi-Objective Decision Making with Distributional Monte Carlo Tree Search—0
Multi-Agent Reinforcement Learning with Temporal Logic SpecificationsCode1
Interpretable Reinforcement Learning Inspired by Piaget's Theory of Cognitive Development—0
Hybrid Beamforming for mmWave MU-MISO Systems Exploiting Multi-agent Deep Reinforcement Learning—0
Hybrid Information-driven Multi-agent Reinforcement Learning—0
Bellman Eluder Dimension: New Rich Classes of RL Problems, and Sample-Efficient Algorithms—0
A Secure Learning Control Strategy via Dynamic Camouflaging for Unknown Dynamical Systems under Attacks—0
Improving Human Decision-Making by Discovering Efficient Strategies for Hierarchical Planning—0
Fast Rates for the Regret of Offline Reinforcement Learning—0
Contextualized Rewriting for Text SummarizationCode1
Deep Reinforcement Learning Aided Monte Carlo Tree Search for MIMO Detection—0
Deep Reinforcement Learning-Based Product Recommender for Online Advertising—0
On the Stability of Random Matrix Product with Markovian Noise: Application to Linear Stochastic Approximation and TD Learning—0
Learning Skills to Navigate without a Master: A Sequential Multi-Policy Reinforcement Learning Algorithm—0
Policy Mirror Descent for Reinforcement Learning: Linear Convergence, New Sampling Complexity, and Generalized Problem Classes—0
Meta-Reinforcement Learning for Reliable Communication in THz/VLC Wireless VR NetworksCode0
Thermal Control of Laser Powder Bed Fusion Using Deep Reinforcement Learning—0
Scalable Voltage Control using Structure-Driven Hierarchical Deep Reinforcement Learning—0
Reinforcement Learning for Freight Booking Control Problems—0
Challenges for Using Impact Regularizers to Avoid Negative Side Effects—0
Counterfactual State Explanations for Reinforcement Learning Agents via Generative Deep LearningCode0
Learning-based vs Model-free Adaptive Control of a MAV under Wind Gust—0
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Benchmark Results

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