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 5226–5250 of 15113 papers

TitleStatusHype
Variational multiscale reinforcement learning for discovering reduced order closure models of nonlinear spatiotemporal transport systems—0
Domain Adapting Deep Reinforcement Learning for Real-world Speech Emotion Recognition—0
gym-DSSAT: a crop model turned into a Reinforcement Learning environment—0
Evaluating Human-like Explanations for Robot Actions in Reinforcement Learning Scenarios—0
DRL-ISP: Multi-Objective Camera ISP with Deep Reinforcement Learning—0
Stochastic optimal well control in subsurface reservoirs using reinforcement learningCode0
Robust optimal well control using an adaptive multi-grid reinforcement learning frameworkCode0
Vessel-following model for inland waterways based on deep reinforcement learning—0
Multi-objective Optimization of Notifications Using Offline Reinforcement Learning—0
Reinforcement Learning for Distributed Transient Frequency Control with Stability and Safety Guarantees—0
Inferring and Conveying Intentionality: Beyond Numerical Rewards to Logical Intentions—0
Instance-Dependent Near-Optimal Policy Identification in Linear MDPs via Online Experiment Design—0
A Learning System for Motion Planning of Free-Float Dual-Arm Space Manipulator towards Non-Cooperative ObjectCode1
Model Selection in Reinforcement Learning with General Function Approximations—0
Reinforcement Learning Portfolio Manager Framework with Monte Carlo Simulation—0
Deep Reinforcement Learning Approach for Trading Automation in The Stock Market—0
Decentralized scheduling through an adaptive, trading-based multi-agent system—0
AVDDPG: Federated reinforcement learning applied to autonomous platoon control—0
Learning Task Embeddings for Teamwork Adaptation in Multi-Agent Reinforcement LearningCode1
Implementing Reinforcement Learning Datacenter Congestion Control in NVIDIA NICsCode1
CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement LearningCode2
Explainability in Deep Reinforcement Learning, a Review into Current Methods and Applications—0
Robust Reinforcement Learning in Continuous Control Tasks with Uncertainty Set RegularizationCode0
Tackling Real-World Autonomous Driving using Deep Reinforcement Learning—0
Resource Allocation in Multicore Elastic Optical Networks: A Deep Reinforcement Learning Approach—0
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Benchmark Results

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