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Found 3 papers, 2 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.
Uncertainty-aware Contact-safe Model-based Reinforcement Learning
In typical MBRL, we cannot expect the data-driven model to generate accurate and reliable policies to the intended robotic tasks during the learning process due to sample scarcity.
Model-based Reinforcement Learning
reinforcement-learning
+1
