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 3841–3850 of 15113 papers

TitleStatusHype
Deep Reinforcement Learning with Shallow Controllers: An Experimental Application to PID Tuning—0
Deep Reinforcement Learning with Robust and Smooth Policy—0
Deep Reinforcement Learning with Smooth Policy—0
Deep Reinforcement Learning with Spiking Q-learning—0
A Modular Test Bed for Reinforcement Learning Incorporation into Industrial Applications—0
Deep Reinforcement Learning with Successor Features for Navigation across Similar Environments—0
Deep Reinforcement Learning with Surrogate Agent-Environment Interface—0
Backpropagation through Time and Space: Learning Numerical Methods with Multi-Agent Reinforcement Learning—0
Deep Reinforcement Learning with Symmetric Prior for Predictive Power Allocation to Mobile Users—0
DisTop: Discovering a Topological representation to learn diverse and rewarding skills—0
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Benchmark Results

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