Model-based Reinforcement Learning with a Hamiltonian Canonical ODE Network. (arXiv:2211.00942v1 [cs.LG])

Model-based reinforcement learning usually suffers from a high sample
complexity in training the world model, especially for the environments with
complex dynamics. To make the training for general physical environments more
efficient, we introduce Hamiltonian canonical ordinary differential equations
into the learning process, which inspires a novel model of neural ordinary
differential auto-encoder (NODA). NODA can model the physical world by nature
and is flexible to impose Hamiltonian mechanics (e.g., the dimension of the
physical equations) which can further accelerate …

arxiv canonical network reinforcement reinforcement learning