Actor-Critic Control with Reference Model Learning
نویسندگان
چکیده
We propose a new actor-critic algorithm for reinforcement learning. The algorithm does not use an explicit actor, but learns a reference model which represents a desired behaviour, along which the process is to be controlled by using the inverse of a learned process model. The algorithm uses Local Linear Regression (LLR) to learn approximations of all the functions involved. The online learning of a process and reference model, in combination with LLR, provides an efficient policy update for faster learning. In addition, the algorithm facilitates the incorporation of prior knowledge. The novel method and a standard actor-critic algorithm are applied to the pendulum swingup problem, in which the novel method achieves faster learning than the standard algorithm.
منابع مشابه
Efficient Model Learning Methods for Actor-Critic Control
We propose two new actor-critic algorithms for reinforcement learning. Both algorithms use local linear regression (LLR) to learn approximations of the functions involved. A crucial feature of the algorithms is that they also learn a process model, and this, in combination with LLR, provides an efficient policy update for faster learning. The first algorithm uses a novel model-based update rule...
متن کاملOnActor-Critic Algorithms
In this article, we propose and analyze a class of actor-critic algorithms. These are two-time-scale algorithms in which the critic uses temporal difference learning with a linearly parameterized approximation architecture, and the actor is updated in an approximate gradient direction, based on information provided by the critic. We show that the features for the critic should ideally span a su...
متن کاملNatural Actor-Critic
This paper investigates a novel model-free reinforcement learning architecture, the Natural Actor-Critic. The actor updates are based on stochastic policy gradients employing Amari’s natural gradient approach, while the critic obtains both the natural policy gradient and additional parameters of a value function simultaneously by linear regression. We show that actor improvements with natural p...
متن کاملSoft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
Model-free deep reinforcement learning (RL) algorithms have been demonstrated on a range of challenging decision making and control tasks. However, these methods typically suffer from two major challenges: very high sample complexity and brittle convergence properties, which necessitate meticulous hyperparameter tuning. Both of these challenges severely limit the applicability of such methods t...
متن کاملAn Analysis of Actor/Critic Algorithms Using Eligibility Traces: Reinforcement Learning with Imperfect Value Function
We present an analysis of actor/critic algorithms, in which the actor updates its policy using eligibility traces of the policy parameters. Most of the theoretical results for eligibility traces have been for only critic's value iteration algorithms. This paper investigates what the actor's eligibility trace does. The results show that the algorithm is an extension of Williams' REINFORCE algori...
متن کامل