Backward Imitation and Forward Reinforcement Learning via Bi-directional Model Rollouts. (arXiv:2208.02434v1 [cs.LG])

Traditional model-based reinforcement learning (RL) methods generate forward
rollout traces using the learnt dynamics model to reduce interactions with the
real environment. The recent model-based RL method considers the way to learn a
backward model that specifies the conditional probability of the previous state
given the previous action and the current state to additionally generate
backward rollout trajectories. However, in this type of model-based method, the
samples derived from backward rollouts and those from forward rollouts are
simply aggregated together …

arxiv bi learning lg reinforcement reinforcement learning