Model-Based Reinforcement Learning with Isolated Imaginations

World models learn the consequences of actions in vision-based interactive systems. However, in practical scenarios like autonomous driving, noncontrollable dynamics that are independent or sparsely dependent on action signals often exist, making it challenging to learn effective world models. To address this issue, we propose Iso-Dream++, a model-based reinforcement learning approach that has two main contributions. First, we optimize the inverse dynamics to encourage the world model to isolate controllable state transitions from the mixed spatiotemporal variations of the environment. Second, we perform policy optimization based on the decoupled latent imaginations, where we roll out noncontrollable states into the future and adaptively associate them with the current controllable state. This enables long-horizon visuomotor control tasks to benefit from isolating mixed dynamics sources in the wild, such as self-driving cars that can anticipate the movement of other vehicles, thereby avoiding potential risks. On top of our previous work, we further consider the sparse dependencies between controllable and noncontrollable states, address the training collapse problem of state decoupling, and validate our approach in transfer learning setups. Our empirical study demonstrates that Iso-Dream++ outperforms existing reinforcement learning models significantly on CARLA and DeepMind Control.Comment: arXiv admin note: substantial text overlap with arXiv:2205.1381

Sim2Real View Invariant Visual Servoing by Recurrent Control

Humans are remarkably proficient at controlling their limbs and tools from a wide range of viewpoints and angles, even in the presence of optical distortions. In robotics, this ability is referred to as visual servoing: moving a tool or end-point to a desired location using primarily visual feedback. In this paper, we study how viewpoint-invariant visual servoing skills can be learned automatically in a robotic manipulation scenario. To this end, we train a deep recurrent controller that can automatically determine which actions move the end-point of a robotic arm to a desired object. The problem that must be solved by this controller is fundamentally ambiguous: under severe variation in viewpoint, it may be impossible to determine the actions in a single feedforward operation. Instead, our visual servoing system must use its memory of past movements to understand how the actions affect the robot motion from the current viewpoint, correcting mistakes and gradually moving closer to the target. This ability is in stark contrast to most visual servoing methods, which either assume known dynamics or require a calibration phase. We show how we can learn this recurrent controller using simulated data and a reinforcement learning objective. We then describe how the resulting model can be transferred to a real-world robot by disentangling perception from control and only adapting the visual layers. The adapted model can servo to previously unseen objects from novel viewpoints on a real-world Kuka IIWA robotic arm. For supplementary videos, see: https://fsadeghi.github.io/Sim2RealViewInvariantServoComment: Supplementary video: https://fsadeghi.github.io/Sim2RealViewInvariantServ

Deep Neural Networks for Visual Reasoning, Program Induction, and Text-to-Image Synthesis.

Deep neural networks excel at pattern recognition, especially in the setting of large scale supervised learning. A combination of better hardware, more data, and algorithmic improvements have yielded breakthroughs in image classification, speech recognition and other perception problems. The research frontier has shifted towards the weak side of neural networks: reasoning, planning, and (like all machine learning algorithms) creativity. How can we advance along this frontier using the same generic techniques so effective in pattern recognition; i.e. gradient descent with backpropagation? In this thesis I develop neural architectures with new capabilities in visual reasoning, program induction and text-to-image synthesis. I propose two models that disentangle the latent visual factors of variation that give rise to images, and enable analogical reasoning in the latent space. I show how to augment a recurrent network with a memory of programs that enables the learning of compositional structure for more data-efficient and generalizable program induction. Finally, I develop a generative neural network that translates descriptions of birds, flowers and other categories into compelling natural images.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/135763/1/reedscot_1.pd

RELATE: Physically Plausible Multi-Object Scene Synthesis Using Structured Latent Spaces

We present RELATE, a model that learns to generate physically plausible scenes and videos of multiple interacting objects. Similar to other generative approaches, RELATE is trained end-to-end on raw, unlabeled data. RELATE combines an object-centric GAN formulation with a model that explicitly accounts for correlations between individual objects. This allows the model to generate realistic scenes and videos from a physically-interpretable parameterization. Furthermore, we show that modeling the object correlation is necessary to learn to disentangle object positions and identity. We find that RELATE is also amenable to physically realistic scene editing and that it significantly outperforms prior art in object-centric scene generation in both synthetic (CLEVR, ShapeStacks) and real-world data (cars). In addition, in contrast to state-of-the-art methods in object-centric generative modeling, RELATE also extends naturally to dynamic scenes and generates videos of high visual fidelity. Source code, datasets and more results are available at http://geometry.cs.ucl.ac.uk/projects/2020/relate/

Efficient Deep Reinforcement Learning via Planning, Generalization, and Improved Exploration

Reinforcement learning (RL) is a general-purpose machine learning framework, which considers an agent that makes sequential decisions in an environment to maximize its reward. Deep reinforcement learning (DRL) approaches use deep neural networks as non-linear function approximators that parameterize policies or value functions directly from raw observations in RL. Although DRL approaches have been shown to be successful on many challenging RL benchmarks, much of the prior work has mainly focused on learning a single task in a model-free setting, which is often sample-inefficient. On the other hand, humans have abilities to acquire knowledge by learning a model of the world in an unsupervised fashion, use such knowledge to plan ahead for decision making, transfer knowledge between many tasks, and generalize to previously unseen circumstances from the pre-learned knowledge. Developing such abilities are some of the fundamental challenges for building RL agents that can learn as efficiently as humans. As a step towards developing the aforementioned capabilities in RL, this thesis develops new DRL techniques to address three important challenges in RL: 1) planning via prediction, 2) rapidly generalizing to new environments and tasks, and 3) efficient exploration in complex environments. The first part of the thesis discusses how to learn a dynamics model of the environment using deep neural networks and how to use such a model for planning in complex domains where observations are high-dimensional. Specifically, we present neural network architectures for action-conditional video prediction and demonstrate improved exploration in RL. In addition, we present a neural network architecture that performs lookahead planning by predicting the future only in terms of rewards and values without predicting observations. We then discuss why this approach is beneficial compared to conventional model-based planning approaches. The second part of the thesis considers generalization to unseen environments and tasks. We first introduce a set of cognitive tasks in a 3D environment and present memory-based DRL architectures that generalize better to previously unseen 3D environments compared to existing baselines. In addition, we introduce a new multi-task RL problem where the agent should learn to execute different tasks depending on given instructions and generalize to new instructions in a zero-shot fashion. We present a new hierarchical DRL architecture that learns to generalize over previously unseen task descriptions with minimal prior knowledge. The third part of the thesis discusses how exploiting past experiences can indirectly drive deep exploration and improve sample-efficiency. In particular, we propose a new off-policy learning algorithm, called self-imitation learning, which learns a policy to reproduce past good experiences. We empirically show that self-imitation learning indirectly encourages the agent to explore reasonably good state spaces and thus significantly improves sample-efficiency on RL domains where exploration is challenging. Overall, the main contribution of this thesis are to explore several fundamental challenges in RL in the context of DRL and develop new DRL architectures and algorithms to address such challenges. This allows us to understand how deep learning can be used to improve sample efficiency, and thus come closer to human-like learning abilities.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145829/1/junhyuk_1.pd