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Workshop: Second Workshop on Quantum Tensor Networks in Machine Learning

Model based multi-agent reinforcement learning with tensor decompositions

Pascal van der Vaart · Anuj Mahajan · Shimon Whiteson


A challenge in multi-agent reinforcement learning is to be able to generalize over intractable state-action spaces. This work achieves generalisation in state-action space over unexplored state-action pairs by modelling the transition and reward functions as tensors of low CP-rank. Initial experiments show that using tensor decompositions in a model-based reinforcement learning algorithm can lead to much faster convergence if the true transition and reward functions are indeed of low rank.