Bounds for Off-policy Prediction in Reinforcement Learning

In this paper, we provide for the first time, error bounds for the off-policy prediction in reinforcement learning. The primary objective in off-policy prediction is to estimate the value function of a given target policy of interest using the linear function approximation architecture by utilizing a sample trajectory generated by a behaviour policy which is possibly different from the target policy. The stability of the off-policy prediction has been an open question for a long time. Only recently, could Yu provide a generalized proof, which makes our results more appealing to the reinforcement learning community. The off-policy prediction is useful in complex reinforcement learning settings, where the sample trajectory is hard to obtain and one has to rely on the sample behaviour of the system with respect to an arbitrary policy. We provide here error bound on the solution of the off-policy prediction with respect to a closeness measure between the target and the behaviour policy

Revisiting the Cross Entropy Method with Applications in Stochastic Global Optimization and Reinforcement Learning

In this paper, we provide a new algorithm for the problem of stochastic global optimization where only noisy versions of the objective function are available. The algorithm is inspired by the well known cross entropy (CE) method. The algorithm takes the shape of a multi-timescale stochastic approximation algorithm, where we reuse the previous samples based on discounted averaging, and hence it saves the overall computational and storage cost. We provide proof of the stability and the global optimization property of our algorithm. The algorithm shows good performance on the noisy versions of global optimization benchmarks and outperforms a state-of-the-art algorithm for non-linear function approximation in reinforcement learning

An incremental off-policy search in a model-free Markov decision process using a single sample path

In this paper, we consider a modified version of the control problem in a model free Markov decision process (MDP) setting with large state and action spaces. The control problem most commonly addressed in the contemporary literature is to find an optimal policy which maximizes the value function, i.e., the long run discounted reward of the MDP. The current settings also assume access to a generative model of the MDP with the hidden premise that observations of the system behaviour in the form of sample trajectories can be obtained with ease from the model. In this paper, we consider a modified version, where the cost function is the expectation of a non-convex function of the value function without access to the generative model. Rather, we assume that a sample trajectory generated using a priori chosen behaviour policy is made available. In this restricted setting, we solve the modified control problem in its true sense, i.e., to find the best possible policy given this limited information. We propose a stochastic approximation algorithm based on the well-known cross entropy method which is data (sample trajectory) efficient, stable, robust as well as computationally and storage efficient. We provide a proof of convergence of our algorithm to a policy which is globally optimal relative to the behaviour policy. We also present experimental results to corroborate our claims and we demonstrate the superiority of the solution produced by our algorithm compared to the state-of-the-art algorithms under appropriately chosen behaviour policy

An online prediction algorithm for reinforcement learning with linear function approximation using cross entropy method

In this paper, we provide two new stable online algorithms for the problem of prediction in reinforcement learning, i.e., estimating the value function of a model-free Markov reward process using the linear function approximation architecture and with memory and computation costs scaling quadratically in the size of the feature set. The algorithms employ the multi-timescale stochastic approximation variant of the very popular cross entropy optimization method which is a model based search method to find the global optimum of a real-valued function. A proof of convergence of the algorithms using the ODE method is provided. We supplement our theoretical results with experimental comparisons. The algorithms achieve good performance fairly consistently on many RL benchmark problems with regards to computational efficiency, accuracy and stability

A Model based Search Method for Prediction in Model-free Markov Decision Process

In this paper, we provide a new algorithm for the problem of prediction in the model-free MDP setting, i.e., estimating the value function of a given policy using the linear function approximation architecture, with memory and computation costs scaling quadratically in the size of the feature set. The algorithm is a multi-timescale variant of the very popular cross entropy (CE) method which is a model based search method to find the global optimum of a real-valued function. This is the first time a model based search method is used for the prediction problem. A proof of convergence using the ODE method is provided. The theoretical results are supplemented with experimental comparisons. The algorithm achieves good performance fairly consistently on many benchmark problems