There is a general trend towards solving problems suited to deep learning with more complex deep learning architectures trained on larger training sets. This requires longer compute times and greater data parallelization or model parallelization. Both data and model parallelism have been historically faster in parameter server architectures, but data parallelism is starting to be faster in ring architectures due to algorithmic improvements. In this paper, we analyze the math behind ring architectures and make an informed adaptation of dynamic scheduling to ring architectures. To do so, we formulate a non-convex, non-linear, NP-hard integer programming problem and a new efficient doubling heuristic for its solution. We build upon Horovod: an open source ring architecture framework over TensorFlow. We show that Horovod jobs have a low cost to stop and restart and that stopping and restarting ring architecture jobs leads to faster completion times. These two facts make dynamic scheduling of ring architecture jobs feasible. Lastly, we simulate a scheduler using these runs and show a more than halving of average job time on some workload patterns.
Modern neural approaches to dependency parsing are trained to predict a tree structure by jointly learning a contextual representation for tokens in a sentence, as well as a headdependent scoring function. Whereas this strategy results in high performance, it is difficult to interpret these representations in relation to the geometry of the underlying tree structure. Our work seeks instead to learn interpretable representations by training a parser to explicitly preserve structural properties of a tree. We do so by casting dependency parsing as a tree embedding problem where we incorporate geometric properties of dependency trees in the form of training losses within a graph-based parser. We provide a thorough evaluation of these geometric losses, showing that the majority of them yield strong tree distance preservation as well as parsing performance on par with a competitive graph-based parser . Finally, we show where parsing errors lie in terms of tree relationship in order to guide future work.
Learned representations in deep reinforcement learning (DRL) have to extract taskrelevant information from complex observations, balancing between robustness to distraction and informativeness to the policy. Such stable and rich representations, often learned via modern function approximation techniques, can enable practical application of the policy improvement theorem, even in high-dimensional continuous state-action spaces. Bisimulation metrics offer one solution to this representation learning problem, by collapsing functionally similar states together in representation space, which promotes invariance to noise and distractors. In this work, we generalize value function approximation bounds for on-policy bisimulation metrics to non-optimal policies and approximate environment dynamics. Our theoretical results help us identify embedding pathologies that may occur in practical use. In particular, we find that these issues stem from an underconstrained dynamics model and an unstable dependence of the embedding norm on the reward signal in environments with sparse rewards. Further, we propose a set of practical remedies: (i) a norm constraint on the representation space, and (ii) an extension of prior approaches with intrinsic rewards and latent space regularization. Finally, we provide evidence that the resulting method is not only more robust to sparse reward functions, but also able to solve challenging continuous control tasks with observational distractions, where prior methods fail.
Bisimulation metrics define a distance measure between states of a Markov decision process (MDP) based on a comparison of reward sequences.Due to this property they provide theoretical guarantees in value function approximation. In this work we first prove that bisimulation metrics can be defined via any p-Wasserstein metric for p ≥ 1. Then we describe an approximate policy iteration (API) procedure that uses ›-aggregation with ı-bisimulation and prove performance bounds for continuous state spaces. We bound the difference between ı-bisimulation metrics in terms of the change in the policies themselves. Based on these theoretical results, we design an API(¸) procedure that employs conservative policy updates and enjoys better performance bounds than the naive API approach. In addition, we propose a novel trust region approach which circumvents the requirement to explicitly solve a constrained optimization problem.Finally, we provide experimental evidence of improved stability compared to non-conservative alternatives in simulated continuous control.
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