How an agent can act optimally in stochastic, partially observable domains is a challenge problem, the standard approach to address this issue is to learn the domain model firstly and then based on the learned model to find the (near) optimal policy. However, offline learning the model often needs to store the entire training data and cannot utilize the data generated in the planning phase. Furthermore, current research usually assumes the learned model is accurate or presupposes knowledge of the nature of the unobservable part of the world. In this paper, for systems with discrete settings, with the benefits of Predictive State Representations (PSRs), a model-based planning approach is proposed where the learning and planning phases can both be executed online and no prior knowledge of the underlying system is required. Experimental results show compared to the state-of-the-art approaches, our algorithm achieved a high level of performance with no prior knowledge provided, along with theoretical advantages of PSRs. Source code is available at this https URL.
When the model of the underlying system is available, model-based planning approaches offer a principled framework for solving the problem of choosing optimal actions, however, most of the related methods assume an accurate model of the underlying system to be known a prior, which is usually unrealistic in real-world applications. The BA-POMDP approach tackles this problem by using a Bayesian approach to model the distribution of all possible models and allows the models to be learned during execution @cite_3 @cite_8 . Unfortunately, BA-POMDPs are limited to some trivial problems as the size of the state space over all possible models is too large to be tractable for non-trivial problems.
@cite_3 @cite_8
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Bayesian learning methods have recently been shown to provide an elegant solution to the exploration-exploitation trade-off in reinforcement learning. However most investigations of Bayesian reinforcement learning to date focus on the standard Markov Decision Processes (MDPs). The primary focus of this paper is to extend these ideas to the case of partially observable domains, by introducing the Bayes-Adaptive Partially Observable Markov Decision Processes. This new framework can be used to simultaneously (1) learn a model of the POMDP domain through interaction with the environment, (2) track the state of the system under partial observability, and (3) plan (near-)optimal sequences of actions. An important contribution of this paper is to provide theoretical results showing how the model can be finitely approximated while preserving good learning performance. We present approximate algorithms for belief tracking and planning in this model, as well as empirical results that illustrate how the model estimate and agent's return improve as a function of experience. Bayesian methods for machine learning have been widely investigated, yielding principled methods for incorporating prior information into inference algorithms. In this survey, we provide an in-depth review of the role of Bayesian methods for the reinforcement learning (RL) paradigm. The major incentives for incorporating Bayesian reasoning in RL are: 1) it provides an elegant approach to action-selection (exploration exploitation) as a function of the uncertainty in learning; and 2) it provides a machinery to incorporate prior knowledge into the algorithms. We first discuss models and methods for Bayesian inference in the simple single-step Bandit model. We then review the extensive recent literature on Bayesian methods for model-based RL, where prior information can be expressed on the parameters of the Markov model. We also present Bayesian methods for model-free RL, where priors are expressed over the value function or policy class. The objective of the paper is to provide a comprehensive survey on Bayesian RL algorithms and their theoretical and empirical properties.