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 5926–5950 of 15113 papers

TitleStatusHype
Towards Interpretable Deep Reinforcement Learning Models via Inverse Reinforcement Learning—0
Marginalized Operators for Off-policy Reinforcement Learning—0
Asynchronous, Option-Based Multi-Agent Policy Gradient: A Conditional Reasoning Approach—0
Text-Driven Video Acceleration: A Weakly-Supervised Reinforcement Learning MethodCode0
On Reinforcement Learning, Effect Handlers, and the State Monad—0
When to Go, and When to Explore: The Benefit of Post-Exploration in Intrinsic Motivation—0
Transformer Network-based Reinforcement Learning Method for Power Distribution Network (PDN) Optimization of High Bandwidth Memory (HBM)—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
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
Adaptive Risk-Tendency: Nano Drone Navigation in Cluttered Environments with Distributional Reinforcement LearningCode1
REPTILE: A Proactive Real-Time Deep Reinforcement Learning Self-adaptive Framework—0
Optimizing Airborne Wind Energy with Reinforcement Learning—0
Image quality assessment for machine learning tasks using meta-reinforcement learning—0
Dynamic Noises of Multi-Agent Environments Can Improve Generalization: Agent-based Models meets Reinforcement Learning—0
A Novel Neuromorphic Processors Realization of Spiking Deep Reinforcement Learning for Portfolio Management—0
Combining Evolution and Deep Reinforcement Learning for Policy Search: a Survey—0
Collaborative Intelligent Reflecting Surface Networks with Multi-Agent Reinforcement Learning—0
Computationally efficient joint coordination of multiple electric vehicle charging points using reinforcement learning—0
Offline Reinforcement Learning Under Value and Density-Ratio Realizability: The Power of Gaps—0
Quasi-Newton Iteration in Deterministic Policy Gradient—0
Reinforcement Learning with Action-Free Pre-Training from VideosCode1
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Benchmark Results

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