Soumya R. Samineni

Utkarsh A. Mishra, Soumya R. Samineni, Prakhar Goel et al.

Recent works in Reinforcement Learning (RL) combine model-free (Mf)-RL algorithms with model-based (Mb)-RL approaches to get the best from both: asymptotic performance of Mf-RL and high sample-efficiency of Mb-RL. Inspired by these works, we propose a hierarchical framework that integrates online learning for the Mb-trajectory optimization with off-policy methods for the Mf-RL. In particular, two loops are proposed, where the Dynamic Mirror Descent based Model Predictive Control (DMD-MPC) is used as the inner loop Mb-RL to obtain an optimal sequence of actions. These actions are in turn used to significantly accelerate the outer loop Mf-RL. We show that our formulation is generic for a broad class of MPC-based policies and objectives, and includes some of the well-known Mb-Mf approaches. We finally introduce a new algorithm: Mirror-Descent Model Predictive RL (M-DeMoRL), which uses Cross-Entropy Method (CEM) with elite fractions for the inner loop. Our experiments show faster convergence of the proposed hierarchical approach on benchmark MuJoCo tasks. We also demonstrate hardware training for trajectory tracking in a 2R leg and hardware transfer for robust walking in a quadruped. We show that the inner-loop Mb-RL significantly decreases the number of training iterations required in the real system, thereby validating the proposed approach.

Soumya Rani Samineni

Recent works in Reinforcement Learning (RL) combine model-free (Mf)-RL algorithms with model-based (Mb)-RL approaches to get the best from both: asymptotic performance of Mf-RL and high sample-efficiency of Mb-RL. Inspired by these works, we propose a hierarchical framework that integrates online learning for the Mb-trajectory optimization with off-policy methods for the Mf-RL. In particular, two loops are proposed, where the Dynamic Mirror Descent based Model Predictive Control (DMD-MPC) is used as the inner loop to obtain an optimal sequence of actions. These actions are in turn used to significantly accelerate the outer loop Mf-RL. We show that our formulation is generic for a broad class of MPC based policies and objectives, and includes some of the well-known Mb-Mf approaches. Based on the framework we define two algorithms to increase sample efficiency of Off Policy RL and to guide end to end RL algorithms for online adaption respectively. Thus we finally introduce two novel algorithms: Dynamic-Mirror Descent Model Predictive RL(DeMoRL), which uses the method of elite fractions for the inner loop and Soft Actor-Critic (SAC) as the off-policy RL for the outer loop and Dynamic-Mirror Descent Model Predictive Layer(DeMo Layer), a special case of the hierarchical framework which guides linear policies trained using Augmented Random Search(ARS). Our experiments show faster convergence of the proposed DeMo RL, and better or equal performance compared to other Mf-Mb approaches on benchmark MuJoCo control tasks. The DeMo Layer was tested on classical Cartpole and custom-built Quadruped trained using Linear Policy.