Composable Model-Free RL for Navigation with Input-Affine Systems
This addresses the problem of safe navigation for autonomous robots in complex, dynamic settings, offering a model-free alternative to existing control methods.
The paper tackles real-time safe navigation for autonomous robots in dynamic environments by proposing a composable, model-free reinforcement learning method that learns policies for individual elements and composes them online, achieving goal reaching and collision avoidance with formal guarantees. Simulations show improved performance over a PPO baseline.
As autonomous robots move into complex, dynamic real-world environments, they must learn to navigate safely in real time, yet anticipating all possible behaviors is infeasible. We propose a composable, model-free reinforcement learning method that learns a value function and an optimal policy for each individual environment element (e.g., goal or obstacle) and composes them online to achieve goal reaching and collision avoidance. Assuming unknown nonlinear dynamics that evolve in continuous time and are input-affine, we derive a continuous-time Hamilton-Jacobi-Bellman (HJB) equation for the value function and show that the corresponding advantage function is quadratic in the action and optimal policy. Based on this structure, we introduce a model-free actor-critic algorithm that learns policies and value functions for static or moving obstacles using gradient descent. We then compose multiple reach/avoid models via a quadratically constrained quadratic program (QCQP), yielding formal obstacle-avoidance guarantees in terms of value-function level sets, providing a model-free alternative to CLF/CBF-based controllers. Simulations demonstrate improved performance over a PPO baseline applied to a discrete-time approximation.