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Found 5 papers, 3 papers with code
Effective Multi-Agent Deep Reinforcement Learning Control with Relative Entropy Regularization
It alleviates the inconsistency of multiple agents' policy updates by introducing the relative entropy regularization to the Centralized Training with Decentralized Execution (CTDE) framework with the Actor-Critic (AC) structure.
Practical Probabilistic Model-based Deep Reinforcement Learning by Integrating Dropout Uncertainty and Trajectory Sampling
Its loss function is designed to correct the fitting error of neural networks for more accurate prediction of probabilistic models.
Remind of the Past: Incremental Learning with Analogical Prompts
It mimics the feature distribution of the target old class on the old model using only samples of new classes.
IA Planner: Motion Planning Using Instantaneous Analysis for Autonomous Vehicle in the Dense Dynamic Scenarios on Highways
In dense and dynamic scenarios, planning a safe and comfortable trajectory is full of challenges when traffic participants are driving at high speed.
Sparse Gaussian Process Based On Hat Basis Functions
Based on hat basis functions, we propose a new sparse Gaussian process method to solve the unconstrained regression problem.
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