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 3271–3280 of 15113 papers

TitleStatusHype
A SUMO Framework for Deep Reinforcement Learning Experiments Solving Electric Vehicle Charging Dispatching Problem—0
DCE: Offline Reinforcement Learning With Double Conservative Estimates—0
DC-MRTA: Decentralized Multi-Robot Task Allocation and Navigation in Complex Environments—0
A Theoretical Analysis of Optimistic Proximal Policy Optimization in Linear Markov Decision Processes—0
Group-Agent Reinforcement Learning—0
DDPG based on multi-scale strokes for financial time series trading strategy—0
Modified DDPG car-following model with a real-world human driving experience with CARLA simulator—0
DDPG++: Striving for Simplicity in Continuous-control Off-Policy Reinforcement Learning—0
A Fast Convergence Theory for Offline Decision Making—0
Deep reinforcement learning for market making in corporate bonds: beating the curse of dimensionality—0
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Benchmark Results

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