Addressing Function Approximation Error in Actor-Critic Methods

In value-based reinforcement learning methods such as deep Q-learning, function approximation errors are known to lead to overestimated value estimates and suboptimal policies. We show that this problem persists in an actor-critic setting and propose novel mechanisms to minimize its effects on both the actor and the critic. Our algorithm builds on Double Q-learning, by taking the minimum value between a pair of critics to limit overestimation. We draw the connection between target networks and overestimation bias, and suggest delaying policy updates to reduce per-update error and further improve performance. We evaluate our method on the suite of OpenAI gym tasks, outperforming the state of the art in every environment tested.Comment: Accepted at ICML 201

Deep Generative Modeling of LiDAR Data

Building models capable of generating structured output is a key challenge for AI and robotics. While generative models have been explored on many types of data, little work has been done on synthesizing lidar scans, which play a key role in robot mapping and localization. In this work, we show that one can adapt deep generative models for this task by unravelling lidar scans into a 2D point map. Our approach can generate high quality samples, while simultaneously learning a meaningful latent representation of the data. We demonstrate significant improvements against state-of-the-art point cloud generation methods. Furthermore, we propose a novel data representation that augments the 2D signal with absolute positional information. We show that this helps robustness to noisy and imputed input; the learned model can recover the underlying lidar scan from seemingly uninformative dataComment: Presented at IROS 201

Machine Learning through Exploration for Perception-Driven Robotics

The ability of robots to perform tasks in human environments has largely been limited to rather simple and specific tasks, such as lawn mowing and vacuum cleaning. As such, current robots are far away from the robot butlers, assistants, and housekeepers that are depicted in science fiction movies. Part of this gap can be explained by the fact that human environments are hugely varied, complex and unstructured. For example, the homes that a domestic robot might end up in are hugely varied. Since every home has a different layout with different objects and furniture, it is impossible for a human designer to anticipate all challenges a robot might face, and equip the robot a priori with all the necessary perceptual and manipulation skills. Instead, robots could be programmed in a way that allows them to adapt to any environment that they are in. In that case, the robot designer would not need to precisely anticipate such environments. The ability to adapt can be provided by robot learning techniques, which can be applied to learn skills for perception and manipulation. Many of the current robot learning techniques, however, rely on human supervisors to provide annotations or demonstrations, and to fine-tuning the methods parameters and heuristics. As such, it can require a significant amount of human time investment to make a robot perform a task in a novel environment, even if statistical learning techniques are used. In this thesis, I focus on another way of obtaining the data a robot needs to learn about the environment and how to successfully perform skills in it. By exploring the environment using its own sensors and actuators, rather than passively waiting for annotations or demonstrations, a robot can obtain this data by itself. I investigate multiple approaches that allow a robot to explore its environment autonomously, while trying to minimize the design effort required to deploy such algorithms in different situations. First, I consider an unsupervised robot with minimal prior knowledge about its environment. It can only learn through observed sensory feedback obtained though interactive exploration of its environment. In a bottom-up, probabilistic approach, the robot tries to segment the objects in its environment through clustering with minimal prior knowledge. This clustering is based on static visual scene features and observed movement. Information theoretic principles are used to autonomously select actions that maximize the expected information gain, and thus learning speed. Our evaluations on a real robot system equipped with an on-board camera show that the proposed method handles noisy inputs better than previous methods, and that action selection according to the information gain criterion does increase the learning speed. Often, however, the goal of a robot is not just to learn the structure of the environment, but to learn how to perform a task encoded by a reward signal. In addition to the weak feedback provided by reward signals, the robot has access to rich sensory data, that, even for simple tasks, is often non-linear and high-dimensional. Sensory data can be leveraged to learn a system model, but in high-dimensional sensory spaces this step often requires manually designing features. I propose a robot reinforcement learning algorithm with learned non-parametric models, value functions, and policies that can deal with high-dimensional state representations. As such, the proposed algorithm is well-suited to deal with high-dimensional signals such as camera images. To avoid that the robot converges prematurely to a sub-optimal solution, the information loss of policy updates is limited. This constraint makes sure the robot keeps exploring the effects of its behavior on the environment. The experiments show that the proposed non-parametric relative entropy policy search algorithm performs better than prior methods that either do not employ bounded updates, or that try to cover the state-space with general-purpose radial basis functions. Furthermore, the method is validated on a real-robot setup with high-dimensional camera image inputs. One problem with typical exploration strategies is that the behavior is perturbed independently in each time step, for example through selecting a random action or random policy parameters. As such, the resulting exploration behavior might be incoherent. Incoherence causes inefficient random walk behavior, makes the system less robust, and causes wear and tear on the robot. A typical solution is to perturb the policy parameters directly, and use the same perturbation for an entire episode. However, this strategy tends to increase the number of episodes needed, since only a single perturbation can be evaluated per episode. I introduce a strategy that can make a more balanced trade-off between the advantages of these two approaches. The experiments show that intermediate trade-offs, rather than independent or episode-based exploration, is beneficial across different tasks and learning algorithms. This thesis thus addresses how robots can learn autonomously by exploring the world through unsupervised learning and reinforcement learning. Throughout the thesis, new approaches and algorithms are introduced: a probabilistic interactive segmentation approach, the non-parametric relative entropy policy search algorithm, and a framework for generalized exploration. To allow the learning algorithms to be applied in different and unknown environments, the design effort and supervision required from human designers or users is minimized. These approaches and algorithms contribute towards the capability of robots to autonomously learn useful skills in human environments in a practical manner

Doubly Stochastic Variational Inference for Neural Processes with Hierarchical Latent Variables

Neural processes (NPs) constitute a family of variational approximate models for stochastic processes with promising properties in computational efficiency and uncertainty quantification. These processes use neural networks with latent variable inputs to induce predictive distributions. However, the expressiveness of vanilla NPs is limited as they only use a global latent variable, while target specific local variation may be crucial sometimes. To address this challenge, we investigate NPs systematically and present a new variant of NP model that we call Doubly Stochastic Variational Neural Process (DSVNP). This model combines the global latent variable and local latent variables for prediction. We evaluate this model in several experiments, and our results demonstrate competitive prediction performance in multi-output regression and uncertainty estimation in classification

Learning of non-parametric control policies with high-dimensional state features

Learning complex control policies from highdimensional sensory input is a challenge for reinforcement learning algorithms. Kernel methods that approximate values functions or transition models can address this problem. Yet, many current approaches rely on instable greedy maximization. In this paper, we develop a policy search algorithm that integrates robust policy updates and kernel embeddings. Our method can learn nonparametric control policies for infinite horizon continuous MDPs with high-dimensional sensory representations. We show that our method outperforms related approaches, and that our algorithm can learn an underpowered swing-up task task directly from highdimensional image data

Learning Objective-Specific Active Learning Strategies with Attentive Neural Processes

Pool-based active learning (AL) is a promising technology for increasing data-efficiency of machine learning models. However, surveys show that performance of recent AL methods is very sensitive to the choice of dataset and training setting, making them unsuitable for general application. In order to tackle this problem, the field Learning Active Learning (LAL) suggests to learn the active learning strategy itself, allowing it to adapt to the given setting. In this work, we propose a novel LAL method for classification that exploits symmetry and independence properties of the active learning problem with an Attentive Conditional Neural Process model. Our approach is based on learning from a myopic oracle, which gives our model the ability to adapt to non-standard objectives, such as those that do not equally weight the error on all data points. We experimentally verify that our Neural Process model outperforms a variety of baselines in these settings. Finally, our experiments show that our model exhibits a tendency towards improved stability to changing datasets. However, performance is sensitive to choice of classifier and more work is necessary to reduce the performance the gap with the myopic oracle and to improve scalability. We present our work as a proof-of-concept for LAL on nonstandard objectives and hope our analysis and modelling considerations inspire future LAL work.Comment: Accepted at ECML 202

Non-parametric policy search with limited information loss

Learning complex control policies from non-linear and redundant sensory input is an important challenge for reinforcement learning algorithms. Non-parametric methods that approximate values functions or transition models can address this problem, by adapting to the complexity of the dataset. Yet, many current non-parametric approaches rely on unstable greedy maximization of approximate value functions, which might lead to poor convergence or oscillations in the policy update. A more robust policy update can be obtained by limiting the information loss between successive state-action distributions. In this paper, we develop a policy search algorithm with policy updates that are both robust and non-parametric. Our method can learn non-parametric control policies for infinite horizon continuous Markov decision processes with non-linear and redundant sensory representations. We investigate how we can use approximations of the kernel function to reduce the time requirements of the demanding non-parametric computations. In our experiments, we show the strong performance of the proposed method, and how it can be approximated efficiently. Finally, we show that our algorithm can learn a real-robot underpowered swing-up task directly from image data

Non-parametric policy search with limited information loss

Learning complex control policies from non-linear and redundant sensory input is an important challenge for reinforcement learning algorithms. Non-parametric methods that approximate values functions or transition models can address this problem, by adapting to the complexity of the data set. Yet, many current non-parametric approaches rely on unstable greedy maximization of approximate value functions, which might lead to poor convergence or oscillations in the policy update. A more robust policy update can be obtained by limiting the information loss between successive state-action distributions. In this paper, we develop a policy search algorithm with policy updates that are both robust and non-parametric. Our method can learn non- parametric control policies for infinite horizon continuous Markov decision processes with non-linear and redundant sensory representations. We investigate how we can use approximations of the kernel function to reduce the time requirements of the demanding non-parametric computations. In our experiments, we show the strong performance of the proposed method, and how it can be approximated efficiently. Finally, we show that our algorithm can learn a real-robot under-powered swing-up task directly from image data

Stochastic Beams and Where to Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement

The well-known Gumbel-Max trick for sampling from a categorical distribution can be extended to sample $k$ elements without replacement. We show how to implicitly apply this 'Gumbel-Top-$k$' trick on a factorized distribution over sequences, allowing to draw exact samples without replacement using a Stochastic Beam Search. Even for exponentially large domains, the number of model evaluations grows only linear in $k$ and the maximum sampled sequence length. The algorithm creates a theoretical connection between sampling and (deterministic) beam search and can be used as a principled intermediate alternative. In a translation task, the proposed method compares favourably against alternatives to obtain diverse yet good quality translations. We show that sequences sampled without replacement can be used to construct low-variance estimators for expected sentence-level BLEU score and model entropy.Comment: ICML 2019 ; 13 pages, 4 figure