Markov Decision Processes (MDPs), the mathematical framework underlying most algorithms in Reinforcement Learning (RL), are often used in a way that wrongfully assumes that the state of an agent's environment does not change during action selection. As RL systems based on MDPs begin to find application in real-world safety critical situations, this mismatch between the assumptions underlying classical MDPs and the reality of real-time computation may lead to undesirable outcomes. In this paper, we introduce a new framework, in which states and actions evolve simultaneously and show how it is related to the classical MDP formulation. We analyze existing algorithms under the new real-time formulation and show why they are suboptimal when used in real-time. We then use those insights to create a new algorithm Real-Time Actor Critic (RTAC) that outperforms the existing state-of-the-art continuous control algorithm Soft Actor Critic both in real-time and non-real-time settings.

Defining a state representation on which optimal control can perform well is a tedious but crucial process. It typically requires expert knowledge, does not generalize straightforwardly over different tasks and strongly influences the quality of the learned controller. In this paper, we present an autonomous feature construction method for learning low-dimensional manifolds of goal-relevant features jointly with an optimal controller using reinforcement learning. Our method combines information-theoretic algorithms with principal component analysis to performs a return-weighted reduction of the state representation. The method does not require any preprocessing of the data, does not assume strong restrictions on the state representation, and substantially improves the performance of learning by reducing the number of samples required. We show that our method can learn high quality controller in redundant spaces, even from pixels, and outperforms both classical and state-of-the-art deep learning approaches.