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 9226–9250 of 15113 papers

TitleStatusHype
Deep Reinforcement Learning for Constrained Field Development Optimization in Subsurface Two-phase Flow—0
Greedy-GQ with Variance Reduction: Finite-time Analysis and Improved Complexity—0
FaiR-IoT: Fairness-aware Human-in-the-Loop Reinforcement Learning for Harnessing Human Variability in Personalized IoT—0
Online Policies for Real-Time Control Using MRAC-RL—0
Reinforcement learning for optimization of variational quantum circuit architectures—0
Reinforcement Learning Beyond Expectation—0
pH-RL: A personalization architecture to bring reinforcement learning to health practice—0
Measuring Sample Efficiency and Generalization in Reinforcement Learning Benchmarks: NeurIPS 2020 Procgen Benchmark—0
Shaping Advice in Deep Multi-Agent Reinforcement LearningCode0
Robust Reinforcement Learning under model misspecificationCode0
Augmenting Automated Game Testing with Deep Reinforcement Learning—0
LASER: Learning a Latent Action Space for Efficient Reinforcement Learning—0
Deep reinforcement learning of event-triggered communication and control for multi-agent cooperative transport—0
Deep Hedging of Derivatives Using Reinforcement Learning—0
Joint Resource Management for MC-NOMA: A Deep Reinforcement Learning Approach—0
KnowRU: Knowledge Reusing via Knowledge Distillation in Multi-agent Reinforcement Learning—0
Self-adaptive Torque Vectoring Controller Using Reinforcement LearningCode0
Reinforcement Learning for Robust Parameterized Locomotion Control of Bipedal Robots—0
Model-Free Learning of Safe yet Effective Controllers—0
A Convex Programming Approach to Data-Driven Risk-Averse Reinforcement Learning—0
Barrier Function-based Safe Reinforcement Learning for Emergency Control of Power Systems—0
Increasing the Efficiency of Policy Learning for Autonomous Vehicles by Multi-Task Representation Learning—0
Autonomous Overtaking in Gran Turismo Sport Using Curriculum Reinforcement Learning—0
Hierarchical Program-Triggered Reinforcement Learning Agents For Automated Driving—0
A Meta-Reinforcement Learning Approach to Process Control—0
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Benchmark Results

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