  Designing a humanoid locomotion controller is challenging and classically
split up in sub-problems. Footstep planning is one of those, where the sequence
of footsteps is defined. Even in simpler environments, finding a minimal
sequence, or even a feasible sequence, yields a complex optimization problem.
In the literature, this problem is usually addressed by search-based algorithms
(e.g. variants of A*). However, such approaches are either computationally
expensive or rely on hand-crafted tuning of several parameters. In this work,
at first, we propose an efficient footstep planning method to navigate in local
environments with obstacles, based on state-of-the art Deep Reinforcement
Learning (DRL) techniques, with very low computational requirements for on-line
inference. Our approach is heuristic-free and relies on a continuous set of
actions to generate feasible footsteps. In contrast, other methods necessitate
the selection of a relevant discrete set of actions. Second, we propose a
forecasting method, allowing to quickly estimate the number of footsteps
required to reach different candidates of local targets. This approach relies
on inherent computations made by the actor-critic DRL architecture. We
demonstrate the validity of our approach with simulation results, and by a
deployment on a kid-size humanoid robot during the RoboCup 2023 competition.
