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 4041–4050 of 15113 papers

TitleStatusHype
Contextual Conservative Q-Learning for Offline Reinforcement Learning—0
A Succinct Summary of Reinforcement Learning—0
Deep Reinforcement Learning for Asset Allocation: Reward Clipping—0
Deep reinforcement learning for irrigation scheduling using high-dimensional sensor feedbackCode0
Safety Filtering for Reinforcement Learning-based Adaptive Cruise Control—0
On the Challenges of using Reinforcement Learning in Precision Drug Dosing: Delay and Prolongedness of Action EffectsCode0
Large-Scale Traffic Signal Control by a Nash Deep Q-network Approach—0
A Policy Optimization Method Towards Optimal-time Stability—0
Learning to Maximize Mutual Information for Dynamic Feature SelectionCode1
Environment Agnostic Representation for Visual Reinforcement LearningCode1
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Benchmark Results

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