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 3021–3030 of 15113 papers

TitleStatusHype
Asymmetric Actor Critic for Image-Based Robot Learning—0
A Greedy Approximation of Bayesian Reinforcement Learning with Probably Optimistic Transition Model—0
Adapting Surprise Minimizing Reinforcement Learning Techniques for Transactive Control—0
Accounting for the Sequential Nature of States to Learn Features for Reinforcement Learning—0
Deep Interactive Bayesian Reinforcement Learning via Meta-Learning—0
Deep Knowledge Based Agent: Learning to do tasks by self-thinking about imaginary worlds—0
Deep Learning of Intrinsically Motivated Options in the Arcade Learning Environment—0
Adapting Sampling Interval of Sensor Networks Using On-Line Reinforcement Learning—0
A Survey on Transformers in Reinforcement Learning—0
A Survey on Traffic Signal Control Methods—0
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Benchmark Results

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