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 3821–3830 of 15113 papers

TitleStatusHype
Correlation Filter Selection for Visual Tracking Using Reinforcement Learning—0
Deep Reinforcement Learning with Explicit Context Representation—0
Assured Learning-enabled Autonomy: A Metacognitive Reinforcement Learning Framework—0
A Model Selection Approach for Corruption Robust Reinforcement Learning—0
ActSafe: Active Exploration with Safety Constraints for Reinforcement Learning—0
Spatio-Temporal Graph Convolutional Neural Networks for Physics-Aware Grid Learning Algorithms—0
Accelerating Self-Imitation Learning from Demonstrations via Policy Constraints and Q-Ensemble—0
Deep Reinforcement Learning with Hybrid Intrinsic Reward Model—0
Assume-Guarantee Reinforcement Learning—0
Correct-by-synthesis reinforcement learning with temporal logic constraints—0
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Benchmark Results

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