Two-phase Selective Decentralization to Improve Reinforcement Learning Systems with MDP

In this paper, we explore the capability of selective decentralization in improving the reinforcement learning performance for unknown systems using model-based approaches. In selective decentralization, we automatically select the best communication policies among agents. Our learning design, which is built on the control system principles, includes two phases. First, we apply system identification to train an approximated model for the unknown systems. Second, we find the suboptimal solution of the Hamilton–Jacobi–Bellman (HJB) equation to derive the suboptimal control. For linear systems, the HJB equation transforms to the well-known Riccati equation with closed-form solution. In nonlinear system, we discretize the approximation model as a Markov Decision Process (MDP) in order to determine the control using dynamic programming algorithms. Since the theoretical foundation of using MDP to control the nonlinear system has not been thoroughly developed, we prove that the control law learned by the discrete-MDP approach is guarantee to stabilize the system, which is the learning goal, given several sufficient conditions. These learning and control techniques could be applied in centralized, completely decentralized and selectively decentralized manner. Our results show that selective decentralization outperforms the complete decentralization and the centralization approaches when the systems are completely decoupled or strongly interconnected

Selectively decentralized reinforcement learning

Indiana University-Purdue University Indianapolis (IUPUI)The main contributions in this thesis include the selectively decentralized method in solving multi-agent reinforcement learning problems and the discretized Markov-decision-process (MDP) algorithm to compute the sub-optimal learning policy in completely unknown learning and control problems. These contributions tackle several challenges in multi-agent reinforcement learning: the unknown and dynamic nature of the learning environment, the difficulty in computing the closed-form solution of the learning problem, the slow learning performance in large-scale systems, and the questions of how/when/to whom the learning agents should communicate among themselves. Through this thesis, the selectively decentralized method, which evaluates all of the possible communicative strategies, not only increases the learning speed, achieves better learning goals but also could learn the communicative policy for each learning agent. Compared to the other state-of-the-art approaches, this thesis’s contributions offer two advantages. First, the selectively decentralized method could incorporate a wide range of well-known algorithms, including the discretized MDP, in single-agent reinforcement learning; meanwhile, the state-of-the-art approaches usually could be applied for one class of algorithms. Second, the discretized MDP algorithm could compute the sub-optimal learning policy when the environment is described in general nonlinear format; meanwhile, the other state-of-the-art approaches often assume that the environment is in limited format, particularly in feedback-linearization form. This thesis also discusses several alternative approaches for multi-agent learning, including Multidisciplinary Optimization. In addition, this thesis shows how the selectively decentralized method could successfully solve several real-worlds problems, particularly in mechanical and biological systems