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 4101–4110 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
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Benchmark Results

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