  In reinforcement learning, it is common to let an agent interact for a fixed
amount of time with its environment before resetting it and repeating the
process in a series of episodes. The task that the agent has to learn can
either be to maximize its performance over (i) that fixed period, or (ii) an
indefinite period where time limits are only used during training to diversify
experience. In this paper, we provide a formal account for how time limits
could effectively be handled in each of the two cases and explain why not doing
so can cause state aliasing and invalidation of experience replay, leading to
suboptimal policies and training instability. In case (i), we argue that the
terminations due to time limits are in fact part of the environment, and thus a
notion of the remaining time should be included as part of the agent's input to
avoid violation of the Markov property. In case (ii), the time limits are not
part of the environment and are only used to facilitate learning. We argue that
this insight should be incorporated by bootstrapping from the value of the
state at the end of each partial episode. For both cases, we illustrate
empirically the significance of our considerations in improving the performance
and stability of existing reinforcement learning algorithms, showing
state-of-the-art results on several control tasks.
