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 3211–3220 of 15113 papers

TitleStatusHype
Asymptotics of Reinforcement Learning with Neural Networks—0
Data Cross-Segmentation for Improved Generalization in Reinforcement Learning Based Algorithmic Trading—0
Data Distillation for Controlling Specificity in Dialogue Generation—0
Asynchronous Actor-Critic for Multi-Agent Reinforcement Learning—0
Data-driven battery operation for energy arbitrage using rainbow deep reinforcement learning—0
Data-driven control of COVID-19 in buildings: a reinforcement-learning approach—0
Data-driven control of micro-climate in buildings: an event-triggered reinforcement learning approach—0
Data-driven control of spatiotemporal chaos with reduced-order neural ODE-based models and reinforcement learning—0
Data-driven Dynamic Multi-objective Optimal Control: An Aspiration-satisfying Reinforcement Learning Approach—0
Adam on Local Time: Addressing Nonstationarity in RL with Relative Adam Timesteps—0
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Benchmark Results

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