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 4091–4100 of 15113 papers

TitleStatusHype
Active Inference as a Model of Agency—0
Active Information Acquisition—0
Active Learning for Risk-Sensitive Inverse Reinforcement Learning—0
Active Learning of Causal Structures with Deep Reinforcement Learning—0
Active Measure Reinforcement Learning for Observation Cost Minimization—0
Active Perception Applied To Unmanned Aerial Vehicles Through Deep Reinforcement Learning—0
Active Perception for Tactile Sensing: A Task-Agnostic Attention-Based Approach—0
Active Perception in Adversarial Scenarios using Maximum Entropy Deep Reinforcement Learning—0
Active Phase-Encode Selection for Slice-Specific Fast MR Scanning Using a Transformer-Based Deep Reinforcement Learning Framework—0
Active Predicting Coding: Brain-Inspired Reinforcement Learning for Sparse Reward Robotic Control Problems—0
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Benchmark Results

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