CACTO-BIC: Scalable Actor–Critic Learning via Biased Sampling and GPU-Accelerated Trajectory Optimization

Trajectory Optimization (TO) and Reinforcement Learning (RL) offer complementary strengths for solving optimal control problems. TO efficiently computes locally optimal solutions but can struggle with non-convexity, while RL is more robust to non-convexity at the cost of significantly higher computational demands. The Continuous Actor–Critic with Trajectory Optimization (CACTO) algorithm was introduced to combine these advantages by learning a warm-start policy that guides the TO solver towards low-cost trajectories. However, scalability remains a key limitation, as increasing system complexity significantly raises the computational cost of TO. This work introduces CACTO with Biased Initial Conditions (CACTO-BIC) to address these challenges. CACTO-BIC improves data efficiency by biasing initial-state sampling leveraging a property of the value function associated with locally optimal policies; moreover, it reduces computation time by exploiting GPU acceleration. Empirical evaluations show improved sample efficiency and faster computation compared to CACTO. Comparisons with Proximal Policy Optimization (PPO) demonstrate that our approach can achieve similar solutions in less time. Finally, experiments on the AlienGO quadruped robot demonstrate that CACTO-BIC, deployed as a receding-horizon planner using a reduced 15D robot model, can scale to high-dimensional systems and is suitable for real-time applications.