Most deep reinforcement learning techniques are unsuitable for robotics, as they require too much interaction time to learn useful, general control policies. This problem can be largely attributed to the fact that a state representation needs to be learned as a part of learning control policies, which can only be done through fitting expected returns based on observed rewards. While the reward function provides information on the desirability of the state of the world, it does not necessarily provide information on how to distill a good, general representation of that state from the sensory observations. State representation learning objectives can be used to help learn such a representation. While many of these objectives have been proposed, they are typically not directly combined with reinforcement learning algorithms. We investigate several methods for integrating state representation learning into reinforcement learning. In these methods, the state representation learning objectives help regularize the state representation during the reinforcement learning, and the reinforcement learning itself is viewed as a crucial state representation learning objective and allowed to help shape the representation. Using autonomous racing tests in the TORCS simulator, we show how the integrated methods quickly learn policies that generalize to new environments much better than deep reinforcement learning without state representation learning.
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