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

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Showing 4101–4125 of 15113 papers

TitleStatusHype
Active Predictive Coding: A Unified Neural Framework for Learning Hierarchical World Models for Perception and Planning—0
Active Privacy-utility Trade-off Against a Hypothesis Testing Adversary—0
Active Reinforcement Learning -- A Roadmap Towards Curious Classifier Systems for Self-Adaptation—0
Active Reinforcement Learning: Observing Rewards at a Cost—0
Active Reinforcement Learning over MDPs—0
Active Reinforcement Learning with Monte-Carlo Tree Search—0
ActiveRIR: Active Audio-Visual Exploration for Acoustic Environment Modeling—0
Active Screening for Recurrent Diseases: A Reinforcement Learning Approach—0
Active search and coverage using point-cloud reinforcement learning—0
CAVEN: An Embodied Conversational Agent for Efficient Audio-Visual Navigation in Noisy Environments—0
Active Vision for Early Recognition of Human Actions—0
Actor-Critic Algorithm for High-dimensional Partial Differential Equations—0
Learning to sample fibers for goodness-of-fit testing—0
Actor-Critic based Improper Reinforcement Learning—0
Actor-Critic Deep Reinforcement Learning for Dynamic Multichannel Access—0
Actor-Critic Deep Reinforcement Learning for Solving Job Shop Scheduling Problems—0
Actor-Critic for Linearly-Solvable Continuous MDP with Partially Known Dynamics—0
Actor-Critic learning for mean-field control in continuous time—0
Actor-Critic Network for O-RAN Resource Allocation: xApp Design, Deployment, and Analysis—0
Actor-Critic Network for Q&A in an Adversarial Environment—0
Actor-Critic Policy Optimization in Partially Observable Multiagent Environments—0
Actor-Critic Reinforcement Learning with Simultaneous Human Control and Feedback—0
Actor-Critic Reinforcement Learning with Phased Actor—0
Actor-Critics Can Achieve Optimal Sample Efficiency—0
Actor-Critic Scheduling for Path-Aware Air-to-Ground Multipath Multimedia Delivery—0
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Benchmark Results

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