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 7101–7125 of 15113 papers

TitleStatusHype
Spatio-Temporal Graph Convolutional Neural Networks for Physics-Aware Grid Learning Algorithms—0
Visual-Tactile Multimodality for Following Deformable Linear Objects Using Reinforcement Learning—0
Robust Meta-Reinforcement Learning with Curriculum-Based Task Sampling—0
TrajGen: Generating Realistic and Diverse Trajectories with Reactive and Feasible Agent Behaviors for Autonomous Driving—0
Mask Atari for Deep Reinforcement Learning as POMDP Benchmarks—0
Reinforcement Learning Guided by Provable Normative ComplianceCode0
Marginalized Operators for Off-policy Reinforcement Learning—0
Multi-Robot Active Mapping via Neural Bipartite Graph Matching—0
Towards Interpretable Deep Reinforcement Learning Models via Inverse Reinforcement Learning—0
Factored Adaptation for Non-Stationary Reinforcement Learning—0
Investigating the Properties of Neural Network Representations in Reinforcement Learning—0
Assessing Evolutionary Terrain Generation Methods for Curriculum Reinforcement Learning—0
Learning to act: a Reinforcement Learning approach to recommend the best next activities—0
Deep Reinforcement Learning for Data-Driven Adaptive Scanning in Ptychography—0
Transformer Network-based Reinforcement Learning Method for Power Distribution Network (PDN) Optimization of High Bandwidth Memory (HBM)—0
Text-Driven Video Acceleration: A Weakly-Supervised Reinforcement Learning MethodCode0
When to Go, and When to Explore: The Benefit of Post-Exploration in Intrinsic Motivation—0
Asynchronous, Option-Based Multi-Agent Policy Gradient: A Conditional Reasoning Approach—0
On Reinforcement Learning, Effect Handlers, and the State Monad—0
REPTILE: A Proactive Real-Time Deep Reinforcement Learning Self-adaptive Framework—0
Deep Reinforcement Learning Aided Platoon Control Relying on V2X Information—0
5G Routing Interfered EnvironmentCode0
Learning Personalized Human-Aware Robot Navigation Using Virtual Reality Demonstrations from a User Study—0
Image quality assessment for machine learning tasks using meta-reinforcement learning—0
Optimizing Airborne Wind Energy with Reinforcement Learning—0
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Benchmark Results

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