Abstract Model-based reinforcement learning approaches leverage a forward dynamics
model to support planning and decision making, which, however, may fail catastrophically if
the model is inaccurate. Although there are several existing methods dedicated to
combating the model error, the potential of the single forward model is still limited. In this
paper, we propose to additionally construct a backward dynamics model to reduce the
reliance on accuracy in forward model predictions. We develop a novel method, called …

Model-Based Reinforcement Learning (MBRL) has been gradually applied in the field of
Robot Learning due to its excellent sample efficiency and asymptotic performance.
However, for high-dimensional learning tasks in complex scenes, the exploration and stable
training capabilities of the robot still need enhancement. In light of policy planning and policy
optimization, we propose a bidirectional model-based policy optimization algorithm based
on adaptive gaussian noise and improved confidence weights (BMPO-NW). The algorithm …