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 3376–3400 of 15113 papers

TitleStatusHype
Augmented Lagrangian-Based Safe Reinforcement Learning Approach for Distribution System Volt/VAR Control—0
DEEP ADVERSARIAL FORWARD MODEL—0
Deep Adversarial Reinforcement Learning for Object Disentangling—0
DeepAGREL: Biologically plausible deep learning via direct reinforcement—0
Deep Anomaly Detection and Search via Reinforcement Learning—0
Deep Apprenticeship Learning for Playing Games—0
Deep-Attack over the Deep Reinforcement Learning—0
COVID-19 Pandemic Cyclic Lockdown Optimization Using Reinforcement Learning—0
Adaptive Control of Differentially Private Linear Quadratic Systems—0
AISYN: AI-driven Reinforcement Learning-Based Logic Synthesis Framework—0
Deep Bellman Hedging—0
Deep Binary Reinforcement Learning for Scalable Verification—0
Cover Tree Bayesian Reinforcement Learning—0
DeepCAS: A Deep Reinforcement Learning Algorithm for Control-Aware Scheduling—0
Deep Coherent Exploration For Continuous Control—0
Accelerating the Learning of TAMER with Counterfactual Explanations—0
Deep Reinforcement Learning in Computer Vision: A Comprehensive Survey—0
Augmenting Automated Game Testing with Deep Reinforcement Learning—0
DeepCQ+: Robust and Scalable Routing with Multi-Agent Deep Reinforcement Learning for Highly Dynamic Networks—0
DeepCrawl: Deep Reinforcement Learning for Turn-based Strategy Games—0
Deep Curiosity Loops in Social Environments—0
Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability—0
Deep Reinforcement Learning in mmW-NOMA: Joint Power Allocation and Hybrid Beamforming—0
Deep differentiable reinforcement learning and optimal trading—0
Deep Reinforcement Learning Models Predict Visual Responses in the Brain: A Preliminary Result—0
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Benchmark Results

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