However, achieving such an assumption is non-trivial, leading to the following two aspects of challenges. On the one hand, it is challenging to design a model with enough capacity with limited computational resources, andexisting models areusually tailored forspecific problems which require heavytrailand-error [25,57,59].

Itisknown that policyevaluation has the interpretation of solving ageneralized Bellman equation. Inthispaper,wederivefinite-sample bounds foranygeneral off-policy TD-like stochastic approximation algorithm that solves for the fixedpoint of this generalized Bellman operator.

In this paper, we focus on testing for safety.

Thus the optimal average reward of the original MDP and modified MDP differ by O ( ϵ). To ensure Assumption 3.1 (b) is satisfied, an aperiodicity transformation can be implemented. The proof of this theorem can be found in [Sch71]. From Lemma 2.2, we thus have, ( J In order to iterate Equation (8), need to ensure the terms are non-negative. Theorem 3.3 presents an upper bound on the error in terms of the average reward.

Reinforcement Learning algorithms can be broadly classified into value-based methods and policy-based methods.

One of the most effectivecontinuous deep reinforcement learning algorithms is normalized advantage functions (NAF).

Empowered by neural networks, deep reinforcement learning (DRL) achieves tremendous empirical success. However, DRL requires a large dataset by interacting with the environment, which is unrealistic in critical scenarios such as autonomous driving and personalized medicine. In this paper, we study how to incorporate the dataset collected in the offline setting to improve the sample efficiency in the online setting. To incorporate the observational data, we face two challenges.