A Learning and Sampling

A.1 Deep generative modelling A complete trajectory is denoted by ζ " t s The log-likelihood function is: Lpθ q " ÿ Applying this simple identiy, we also have: 0 " E On the other hand, it discourages action samples directly sampled from the prior. To ensure the transition model's validity, it needs to be grounded in real-world dynamics when jointly learned with the policy. Otherwise, the agent would be purely hallucinating based on the demonstrations. It would not be a problem if the action space is quantized. Intuitively, action samples at each step are updated with the energy of all subsequent actions and a single-step forward by back-propagation. To train the policy, Eq. (8) can now be rewritten as δ Eq. (5) is an empirical estimate of E We first prove the construction above is valid at optimality.