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 8451–8475 of 15113 papers

TitleStatusHype
Multiple Tasks Integration: Tagging, Syntactic and Semantic Parsing as a Single Task—0
Optimization Algorithm for Feedback and Feedforward Policies towards Robot Control Robust to Sensing Failures—0
Trajectory Tracking of Underactuated Sea Vessels With Uncertain Dynamics: An Integral Reinforcement Learning Approach—0
AdaPool: A Diurnal-Adaptive Fleet Management Framework using Model-Free Deep Reinforcement Learning and Change Point Detection—0
Deep Reinforcement Learning for Constrained Field Development Optimization in Subsurface Two-phase Flow—0
Generalized Reinforcement Learning for Building Control using Behavioral Cloning—0
RLAD: Time Series Anomaly Detection through Reinforcement Learning and Active Learning—0
Solving Heterogeneous General Equilibrium Economic Models with Deep Reinforcement Learning—0
Simultaneous Navigation and Construction Benchmarking EnvironmentsCode1
DEALIO: Data-Efficient Adversarial Learning for Imitation from Observation—0
Energy Efficient Edge Computing: When Lyapunov Meets Distributed Reinforcement Learning—0
Co-Adaptation of Algorithmic and Implementational Innovations in Inference-based Deep Reinforcement LearningCode1
FaiR-IoT: Fairness-aware Human-in-the-Loop Reinforcement Learning for Harnessing Human Variability in Personalized IoT—0
Towards Real-World Deployment of Reinforcement Learning for Traffic Signal ControlCode1
Greedy-GQ with Variance Reduction: Finite-time Analysis and Improved Complexity—0
Online Policies for Real-Time Control Using MRAC-RL—0
Reinforcement learning for optimization of variational quantum circuit architectures—0
Deep Reinforcement Learning for Resource Allocation in Business ProcessesCode1
Deep Hedging of Derivatives Using Reinforcement Learning—0
Reinforcement Learning Beyond Expectation—0
pH-RL: A personalization architecture to bring reinforcement learning to health practice—0
Shaping Advice in Deep Multi-Agent Reinforcement LearningCode0
Augmenting Automated Game Testing with Deep Reinforcement Learning—0
Joint Resource Management for MC-NOMA: A Deep Reinforcement Learning Approach—0
Deep reinforcement learning of event-triggered communication and control for multi-agent cooperative transport—0
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Benchmark Results

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