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 4291–4300 of 15113 papers

TitleStatusHype
A Deep Reinforcement Learning Framework for Continuous Intraday Market Bidding—0
A Deep Reinforcement Learning Framework for Contention-Based Spectrum Sharing—0
A Deep Reinforcement Learning Framework for Rapid Diagnosis of Whole Slide Pathological Images—0
A Deep Reinforcement Learning Framework for Optimizing Congestion Control in Data Centers—0
A deep reinforcement learning model based on deterministic policy gradient for collective neural crest cell migration—0
A deep reinforcement learning model for predictive maintenance planning of road assets: Integrating LCA and LCCA—0
A Deep Reinforcement Learning Strategy for UAV Autonomous Landing on a Platform—0
A Deep Reinforcement Learning Trader without Offline Training—0
A Deep Value-network Based Approach for Multi-Driver Order Dispatching—0
A Definition of Happiness for Reinforcement Learning Agents—0
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Benchmark Results

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