  Model-based reinforcement learning (RL) is more sample efficient than
model-free RL by using imaginary trajectories generated by the learned dynamics
model. When the model is inaccurate or biased, imaginary trajectories may be
deleterious for training the action-value and policy functions. To alleviate
such problem, this paper proposes to adaptively reweight the imaginary
transitions, so as to reduce the negative effects of poorly generated
trajectories. More specifically, we evaluate the effect of an imaginary
transition by calculating the change of the loss computed on the real samples
when we use the transition to train the action-value and policy functions.
Based on this evaluation criterion, we construct the idea of reweighting each
imaginary transition by a well-designed meta-gradient algorithm. Extensive
experimental results demonstrate that our method outperforms state-of-the-art
model-based and model-free RL algorithms on multiple tasks. Visualization of
our changing weights further validates the necessity of utilizing reweight
scheme.
