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
Resilient Legged Local Navigation: Learning to Traverse with Compromised Perception End-to-End—0
Resilient robot teams: a review integrating decentralised control, change-detection, and learning—0
Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning—0
Resmax: An Alternative Soft-Greedy Operator for Reinforcement Learning—0
Resolving Causal Confusion in Reinforcement Learning via Robust Exploration—0
Resolving Congestions in the Air Traffic Management Domain via Multiagent Reinforcement Learning Methods—0
Resolving Latency and Inventory Risk in Market Making with Reinforcement Learning—0
Resource Abstraction for Reinforcement Learning in Multiagent Congestion Problems—0
Resource Allocation for a Wireless Coexistence Management System Based on Reinforcement Learning—0
Resource Allocation in Mobility-Aware Federated Learning Networks: A Deep Reinforcement Learning Approach—0
Resource Allocation in Multicore Elastic Optical Networks: A Deep Reinforcement Learning Approach—0
The state-of-the-art review on resource allocation problem using artificial intelligence methods on various computing paradigms—0
Resource Constrained Deep Reinforcement Learning—0
Resource-Constrained Station-Keeping for Helium Balloons using Reinforcement Learning—0
Resource Governance in Networked Systems via Integrated Variational Autoencoders and Reinforcement Learning—0
Resource Management for Blockchain-enabled Federated Learning: A Deep Reinforcement Learning Approach—0
Resource Management in Wireless Networks via Multi-Agent Deep Reinforcement Learning—0
Resource Optimization for Tail-Based Control in Wireless Networked Control Systems—0
Responding to Illegal Activities Along the Canadian Coastlines Using Reinforcement Learning—0
Response to Comment on 'Perceptual Learning Incepted by Decoded fMRI Neurofeedback Without Stimulus Presentation'; How can a decoded neurofeedback method (DecNef) lead to successful reinforcement and visual perceptual learning?—0
Responsive Safety in Reinforcement Learning by PID Lagrangian Methods—0
Responsive Safety in Reinforcement Learning—0
Restarted Bayesian Online Change-point Detection for Non-Stationary Markov Decision Processes—0
Restoring Chaos Using Deep Reinforcement Learning—0
Rethink AI-based Power Grid Control: Diving Into Algorithm Design—0
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Benchmark Results

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