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Multi-Agent Learning of Numerical Methods for Hyperbolic PDEs with Factored Dec-MDP
Factored decentralized Markov decision process (Dec-MDP) is a framework for modeling sequential decision making problems in multi-agent systems.
Backpropagation through Time and Space: Learning Numerical Methods with Multi-Agent Reinforcement Learning
We introduce Backpropagation Through Time and Space (BPTTS), a method for training a recurrent spatio-temporal neural network, that is used in a homogeneous multi-agent reinforcement learning (MARL) setting to learn numerical methods for hyperbolic conservation laws.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Diverse Exploration via Conjugate Policies for Policy Gradient Methods
We address the challenge of effective exploration while maintaining good performance in policy gradient methods.
