  Model-based reinforcement learning (MBRL) with model-predictive control or
online planning has shown great potential for locomotion control tasks in terms
of both sample efficiency and asymptotic performance. Despite their initial
successes, the existing planning methods search from candidate sequences
randomly generated in the action space, which is inefficient in complex
high-dimensional environments. In this paper, we propose a novel MBRL
algorithm, model-based policy planning (POPLIN), that combines policy networks
with online planning. More specifically, we formulate action planning at each
time-step as an optimization problem using neural networks. We experiment with
both optimization w.r.t. the action sequences initialized from the policy
network, and also online optimization directly w.r.t. the parameters of the
policy network. We show that POPLIN obtains state-of-the-art performance in the
MuJoCo benchmarking environments, being about 3x more sample efficient than the
state-of-the-art algorithms, such as PETS, TD3 and SAC. To explain the
effectiveness of our algorithm, we show that the optimization surface in
parameter space is smoother than in action space. Further more, we found the
distilled policy network can be effectively applied without the expansive model
predictive control during test time for some environments such as Cheetah. Code
is released in https://github.com/WilsonWangTHU/POPLIN.
