Mega-Reward: Achieving Human-Level Play without Extrinsic Rewards

Intrinsic rewards were introduced to simulate how human intelligence works; they are usually evaluated by intrinsically-motivated play, i.e., playing games without extrinsic rewards but evaluated with extrinsic rewards. However, none of the existing intrinsic reward approaches can achieve human-level performance under this very challenging setting of intrinsically-motivated play. In this work, we propose a novel megalomania-driven intrinsic reward (called mega-reward), which, to our knowledge, is the first approach that achieves human-level performance in intrinsically-motivated play. Intuitively, mega-reward comes from the observation that infants' intelligence develops when they try to gain more control on entities in an environment; therefore, mega-reward aims to maximize the control capabilities of agents on given entities in a given environment. To formalize mega-reward, a relational transition model is proposed to bridge the gaps between direct and latent control. Experimental studies show that mega-reward (i) can greatly outperform all state-of-the-art intrinsic reward approaches, (ii) generally achieves the same level of performance as Ex-PPO and professional human-level scores, and (iii) has also a superior performance when it is incorporated with extrinsic rewards

State Representation Learning for Control: An Overview

Representation learning algorithms are designed to learn abstract features that characterize data. State representation learning (SRL) focuses on a particular kind of representation learning where learned features are in low dimension, evolve through time, and are influenced by actions of an agent. The representation is learned to capture the variation in the environment generated by the agent's actions; this kind of representation is particularly suitable for robotics and control scenarios. In particular, the low dimension characteristic of the representation helps to overcome the curse of dimensionality, provides easier interpretation and utilization by humans and can help improve performance and speed in policy learning algorithms such as reinforcement learning. This survey aims at covering the state-of-the-art on state representation learning in the most recent years. It reviews different SRL methods that involve interaction with the environment, their implementations and their applications in robotics control tasks (simulated or real). In particular, it highlights how generic learning objectives are differently exploited in the reviewed algorithms. Finally, it discusses evaluation methods to assess the representation learned and summarizes current and future lines of research

On discovering and learning structure under limited supervision

Les formes, les surfaces, les événements et les objets (vivants et non vivants) constituent le monde. L'intelligence des agents naturels, tels que les humains, va au-delà de la simple reconnaissance de formes. Nous excellons à construire des représentations et à distiller des connaissances pour comprendre et déduire la structure du monde. Spécifiquement, le développement de telles capacités de raisonnement peut se produire même avec une supervision limitée. D'autre part, malgré son développement phénoménal, les succès majeurs de l'apprentissage automatique, en particulier des modèles d'apprentissage profond, se situent principalement dans les tâches qui ont accès à de grands ensembles de données annotées. Dans cette thèse, nous proposons de nouvelles solutions pour aider à combler cette lacune en permettant aux modèles d'apprentissage automatique d'apprendre la structure et de permettre un raisonnement efficace en présence de tâches faiblement supervisés. Le thème récurrent de la thèse tente de s'articuler autour de la question « Comment un système perceptif peut-il apprendre à organiser des informations sensorielles en connaissances utiles sous une supervision limitée ? » Et il aborde les thèmes de la géométrie, de la composition et des associations dans quatre articles distincts avec des applications à la vision par ordinateur (CV) et à l'apprentissage par renforcement (RL). Notre première contribution ---Pix2Shape---présente une approche basée sur l'analyse par synthèse pour la perception. Pix2Shape exploite des modèles génératifs probabilistes pour apprendre des représentations 3D à partir d'images 2D uniques. Le formalisme qui en résulte nous offre une nouvelle façon de distiller l'information d'une scène ainsi qu'une représentation puissantes des images. Nous y parvenons en augmentant l'apprentissage profond non supervisé avec des biais inductifs basés sur la physique pour décomposer la structure causale des images en géométrie, orientation, pose, réflectance et éclairage. Notre deuxième contribution ---MILe--- aborde les problèmes d'ambiguïté dans les ensembles de données à label unique tels que ImageNet. Il est souvent inapproprié de décrire une image avec un seul label lorsqu'il est composé de plus d'un objet proéminent. Nous montrons que l'intégration d'idées issues de la littérature linguistique cognitive et l'imposition de biais inductifs appropriés aident à distiller de multiples descriptions possibles à l'aide d'ensembles de données aussi faiblement étiquetés. Ensuite, nous passons au paradigme d'apprentissage par renforcement, et considérons un agent interagissant avec son environnement sans signal de récompense. Notre troisième contribution ---HaC--- est une approche non supervisée basée sur la curiosité pour apprendre les associations entre les modalités visuelles et tactiles. Cela aide l'agent à explorer l'environnement de manière autonome et à utiliser davantage ses connaissances pour s'adapter aux tâches en aval. La supervision dense des récompenses n'est pas toujours disponible (ou n'est pas facile à concevoir), dans de tels cas, une exploration efficace est utile pour générer un comportement significatif de manière auto-supervisée. Pour notre contribution finale, nous abordons l'information limitée contenue dans les représentations obtenues par des agents RL non supervisés. Ceci peut avoir un effet néfaste sur la performance des agents lorsque leur perception est basée sur des images de haute dimension. Notre approche a base de modèles combine l'exploration et la planification sans récompense pour affiner efficacement les modèles pré-formés non supervisés, obtenant des résultats comparables à un agent entraîné spécifiquement sur ces tâches. Il s'agit d'une étape vers la création d'agents capables de généraliser rapidement à plusieurs tâches en utilisant uniquement des images comme perception.Shapes, surfaces, events, and objects (living and non-living) constitute the world. The intelligence of natural agents, such as humans is beyond pattern recognition. We excel at building representations and distilling knowledge to understand and infer the structure of the world. Critically, the development of such reasoning capabilities can occur even with limited supervision. On the other hand, despite its phenomenal development, the major successes of machine learning, in particular, deep learning models are primarily in tasks that have access to large annotated datasets. In this dissertation, we propose novel solutions to help address this gap by enabling machine learning models to learn the structure and enable effective reasoning in the presence of weakly supervised settings. The recurring theme of the thesis tries to revolve around the question of "How can a perceptual system learn to organize sensory information into useful knowledge under limited supervision?" And it discusses the themes of geometry, compositions, and associations in four separate articles with applications to computer vision (CV) and reinforcement learning (RL). Our first contribution ---Pix2Shape---presents an analysis-by-synthesis based approach(also referred to as inverse graphics) for perception. Pix2Shape leverages probabilistic generative models to learn 3D-aware representations from single 2D images. The resulting formalism allows us to perform a novel view synthesis of a scene and produce powerful representations of images. We achieve this by augmenting unsupervised learning with physically based inductive biases to decompose a scene structure into geometry, pose, reflectance and lighting. Our Second contribution ---MILe--- addresses the ambiguity issues in single-labeled datasets such as ImageNet. It is often inappropriate to describe an image with a single label when it is composed of more than one prominent object. We show that integrating ideas from Cognitive linguistic literature and imposing appropriate inductive biases helps in distilling multiple possible descriptions using such weakly labeled datasets. Next, moving into the RL setting, we consider an agent interacting with its environment without a reward signal. Our third Contribution ---HaC--- is a curiosity based unsupervised approach to learning associations between visual and tactile modalities. This aids the agent to explore the environment in an analogous self-guided fashion and further use this knowledge to adapt to downstream tasks. In the absence of reward supervision, intrinsic movitivation is useful to generate meaningful behavior in a self-supervised manner. In our final contribution, we address the representation learning bottleneck in unsupervised RL agents that has detrimental effect on the performance on high-dimensional pixel based inputs. Our model-based approach combines reward-free exploration and planning to efficiently fine-tune unsupervised pre-trained models, achieving comparable results to task-specific baselines. This is a step towards building agents that can generalize quickly on more than a single task using image inputs alone

Towards Practical Multi-Object Manipulation using Relational Reinforcement Learning

Learning robotic manipulation tasks using reinforcement learning with sparse rewards is currently impractical due to the outrageous data requirements. Many practical tasks require manipulation of multiple objects, and the complexity of such tasks increases with the number of objects. Learning from a curriculum of increasingly complex tasks appears to be a natural solution, but unfortunately, does not work for many scenarios. We hypothesize that the inability of the state-of-the-art algorithms to effectively utilize a task curriculum stems from the absence of inductive biases for transferring knowledge from simpler to complex tasks. We show that graph-based relational architectures overcome this limitation and enable learning of complex tasks when provided with a simple curriculum of tasks with increasing numbers of objects. We demonstrate the utility of our framework on a simulated block stacking task. Starting from scratch, our agent learns to stack six blocks into a tower. Despite using step-wise sparse rewards, our method is orders of magnitude more data-efficient and outperforms the existing state-of-the-art method that utilizes human demonstrations. Furthermore, the learned policy exhibits zero-shot generalization, successfully stacking blocks into taller towers and previously unseen configurations such as pyramids, without any further training.Comment: 10 pages, 4 figures and 1 table in main article, 3 figures and 3 tables in appendix. Supplementary website and videos at https://richardrl.github.io/relational-rl