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 6526–6550 of 15113 papers

TitleStatusHype
Vessel-following model for inland waterways based on 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
Variational multiscale reinforcement learning for discovering reduced order closure models of nonlinear spatiotemporal transport systems—0
Multi-objective Optimization of Notifications Using Offline Reinforcement Learning—0
Evaluating Human-like Explanations for Robot Actions in Reinforcement Learning Scenarios—0
gym-DSSAT: a crop model turned into a Reinforcement Learning environment—0
Domain Adapting Deep Reinforcement Learning for Real-world Speech Emotion Recognition—0
DRL-ISP: Multi-Objective Camera ISP with Deep Reinforcement Learning—0
Instance-Dependent Near-Optimal Policy Identification in Linear MDPs via Online Experiment Design—0
Inferring and Conveying Intentionality: Beyond Numerical Rewards to Logical Intentions—0
Model Selection in Reinforcement Learning with General Function Approximations—0
Reinforcement Learning Portfolio Manager Framework with Monte Carlo Simulation—0
Resource Allocation in Multicore Elastic Optical Networks: A Deep Reinforcement Learning Approach—0
Tackling Real-World Autonomous Driving using Deep Reinforcement Learning—0
Robust Reinforcement Learning in Continuous Control Tasks with Uncertainty Set RegularizationCode0
Planning with RL and episodic-memory behavioral priors—0
Decentralized scheduling through an adaptive, trading-based multi-agent system—0
Deep Reinforcement Learning Approach for Trading Automation in The Stock Market—0
Explainability in Deep Reinforcement Learning, a Review into Current Methods and Applications—0
AVDDPG: Federated reinforcement learning applied to autonomous platoon control—0
General Policy Evaluation and Improvement by Learning to Identify Few But Crucial StatesCode0
Safe Reinforcement Learning via Confidence-Based Filters—0
USHER: Unbiased Sampling for Hindsight Experience ReplayCode0
Renaissance Robot: Optimal Transport Policy Fusion for Learning Diverse SkillsCode0
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Benchmark Results

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