We develop two new online actor-critic control algorithms with adaptive feature tuning for Markov Decision Processes (MDPs). One of our algorithms is proposed for the long-run average cost objective, while the other works for discounted cost MDPs. Our actor-critic architecture incorporates parameterization both in the policy and the value function. A gradient search in the policy parameters is performed to improve the performance of the actor. The computation of the aforementioned gradient, however, requires an estimate of the value function of the policy corresponding to the current actor parameter. The value function, on the other hand, is approximated using linear function approximation and obtained from the critic. The error in approximation of the value function, however, results in suboptimal policies. In our article, we also update the features by performing a gradient descent on the Grassmannian of features to minimize a mean square Bellman error objective in order to find the best features. The aim is to obtain a good approximation of the value function and thereby ensure convergence of the actor to locally optimal policies. In order to estimate the gradient of the objective in the case of the average cost criterion, we utilize the policy gradient theorem, while in the case of the discounted cost objective, we utilize the simultaneous perturbation stochastic approximation (SPSA) scheme. We prove that our actor-critic algorithms converge to locally optimal policies. Experiments on two different settings show performance improvements resulting from our feature adaptation scheme.
['K J Prabuchandran', 'Shalabh Bhatnagar', 'Vivek S. Borkar']
Actor-Critic Algorithms with Online Feature Adaptation