Maximilian Igl

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  • Multitask Soft Option Learning

    We present Multitask Soft Option Learning (MSOL), a hierarchical multitask framework based on Planning as Inference. MSOL extends the concept of options, using separate variational posteriors for each task, regularized by a shared prior. This allows fine-tuning of options for new tasks without forgetting their learned policies, leading to faster training without reducing the expressiveness of the hierarchical policy. Additionally, MSOL avoids several instabilities during training in a multitask setting and provides a natural way to not only learn intra-option policies, but also their terminations. We demonstrate empirically that MSOL significantly outperforms both hierarchical and flat transfer-learning baselines in challenging multi-task environments.

    04/01/2019 ∙ by Maximilian Igl, et al. ∙ 98 share

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  • Deep Variational Reinforcement Learning for POMDPs

    Many real-world sequential decision making problems are partially observable by nature, and the environment model is typically unknown. Consequently, there is great need for reinforcement learning methods that can tackle such problems given only a stream of incomplete and noisy observations. In this paper, we propose deep variational reinforcement learning (DVRL), which introduces an inductive bias that allows an agent to learn a generative model of the environment and perform inference in that model to effectively aggregate the available information. We develop an n-step approximation to the evidence lower bound (ELBO), allowing the model to be trained jointly with the policy. This ensures that the latent state representation is suitable for the control task. In experiments on Mountain Hike and flickering Atari we show that our method outperforms previous approaches relying on recurrent neural networks to encode the past.

    06/06/2018 ∙ by Maximilian Igl, et al. ∙ 2 share

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  • Auto-Encoding Sequential Monte Carlo

    We introduce AESMC: a method for using deep neural networks for simultaneous model learning and inference amortization in a broad family of structured probabilistic models. Starting with an unlabeled dataset and a partially specified underlying generative model, AESMC refines the generative model and learns efficient proposal distributions for SMC for performing inference in this model. Our approach relies on 1) efficiency of SMC in performing inference in structured probabilistic models and 2) flexibility of deep neural networks to model complex conditional probability distributions. We demonstrate that our approach provides a fast, accurate, easy-to-implement, and scalable means for carrying out parameter estimation in high-dimensional statistical models as well as simultaneous model learning and proposal amortization in neural network based models.

    05/29/2017 ∙ by Tuan Anh Le, et al. ∙ 0 share

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  • TreeQN and ATreeC: Differentiable Tree Planning for Deep Reinforcement Learning

    Combining deep model-free reinforcement learning with on-line planning is a promising approach to building on the successes of deep RL. On-line planning with look-ahead trees has proven successful in environments where transition models are known a priori. However, in complex environments where transition models need to be learned from data, the deficiencies of learned models have limited their utility for planning. To address these challenges, we propose TreeQN, a differentiable, recursive, tree-structured model that serves as a drop-in replacement for any value function network in deep RL with discrete actions. TreeQN dynamically constructs a tree by recursively applying a transition model in a learned abstract state space and then aggregating predicted rewards and state-values using a tree backup to estimate Q-values. We also propose ATreeC, an actor-critic variant that augments TreeQN with a softmax layer to form a stochastic policy network. Both approaches are trained end-to-end, such that the learned model is optimised for its actual use in the planner. We show that TreeQN and ATreeC outperform n-step DQN and A2C on a box-pushing task, as well as n-step DQN and value prediction networks (Oh et al., 2017) on multiple Atari games, with deeper trees often outperforming shallower ones. We also present a qualitative analysis that sheds light on the trees learned by TreeQN.

    10/31/2017 ∙ by Gregory Farquhar, et al. ∙ 0 share

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  • Tighter Variational Bounds are Not Necessarily Better

    We provide theoretical and empirical evidence that using tighter evidence lower bounds (ELBOs) can be detrimental to the process of learning an inference network by reducing the signal-to-noise ratio of the gradient estimator. Our results call into question common implicit assumptions that tighter ELBOs are better variational objectives for simultaneous model learning and inference amortization schemes. Based on our insights, we introduce three new algorithms: the partially importance weighted auto-encoder (PIWAE), the multiply importance weighted auto-encoder (MIWAE), and the combination importance weighted auto-encoder (CIWAE), each of which includes the standard importance weighted auto-encoder (IWAE) as a special case. We show that each can deliver improvements over IWAE, even when performance is measured by the IWAE target itself. Moreover, PIWAE can simultaneously deliver improvements in both the quality of the inference network and generative network, relative to IWAE.

    02/13/2018 ∙ by Tom Rainforth, et al. ∙ 0 share

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