Efficient Actor-Critic Algorithm with Hierarchical Model Learning and Planning

Abstract

To improve the convergence rate and the sample efficiency, two efficient learning methods AC-HMLP and RAC-HMLP (AC-HMLP with ℓ 2-regularization) are proposed by combining actor-critic algorithm with hierarchical model learning and planning. The hierarchical models consisting of the local and the global models, which are learned at the same time during learning of the value function and the policy, are approximated by local linear regression (LLR) and linear function approximation (LFA), respectively. Both the local model and the global model are applied to generate samples for planning; the former is used only if the state-prediction error does not surpass the threshold at each time step, while the latter is utilized at the end of each episode. The purpose of taking both models is to improve the sample efficiency and accelerate the convergence rate of the whole algorithm through fully utilizing the local and global information. Experimentally, AC-HMLP and RAC-HMLP are compared with three representative algorithms on two Reinforcement Learning (RL) benchmark problems. The results demonstrate that they perform best in terms of convergence rate and sample efficiency.

Action-Based Load Balancing Technique in Cloud Network Using Actor-Critic-Swarm Optimization

“…In this process, VM is an agent and server is the environment, where VM takes an action by interacting with the server at each cycle. MDP can be represented as four-tuple ðS, A, P, RÞ, and these are described as follows [22]: S representing as state space: s t ∊S denotes the state of the server at time period t.…”