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Directly Attention Loss Adjusted Prioritized Experience Replay
Prioritized Experience Replay (PER) enables the model to learn more about relatively important samples by artificially changing their accessed frequencies.
QFree: A Universal Value Function Factorization for Multi-Agent Reinforcement Learning
Once a joint policy is obtained, it is critical to design a value function factorization method to extract optimal decentralized policies for the agents, which needs to satisfy the individual-global-max (IGM) principle.
Attention Loss Adjusted Prioritized Experience Replay
Prioritized Experience Replay (PER) is a technical means of deep reinforcement learning by selecting experience samples with more knowledge quantity to improve the training rate of neural network.
Economic MPC-based planning for marine vehicles: Tuning safety and energy efficiency
Energy efficiency and safety are two critical objectives for marine vehicles operating in environments with obstacles, and they generally conflict with each other.
Stochastic Model Predictive Control for Linear Systems with Unbounded Additive Uncertainties
Secondly, by utilizing the probabilistic positively invariant set, the probabilistically resolvable constant tube-based stochastic model predictive control algorithm is developed by employing the constantly tightened constraints in the entire prediction horizons.
