Learning and planning in videogames via task decomposition

Artificial intelligence (AI) methods have come a long way in tabletop games, with computer programs having now surpassed human experts in the challenging games of chess, Go and heads-up no-limit Texas hold'em. However, a significant simplifying factor in these games is that individual decisions have a relatively large impact on the state of the game. The real world, however, is granular. Human beings are continually presented with new information and are faced with making a multitude of tiny decisions every second. Viewed in these terms, feedback is often sparse, meaning that it only arrives after one has made a great number of decisions. Moreover, in many real-world problems there is a continuous range of actions to choose from, and attaining meaningful feedback from the environment often requires a strong degree of action coordination. Videogames, in which players must likewise contend with granular time scales and continuous action spaces, are in this sense a better proxy for real-world problems, and have thus become regarded by many as the new frontier in games AI. Seemingly, the way in which human players approach granular decision-making in videogames is by decomposing complex tasks into high-level subproblems, thereby allowing them to focus on the "big picture". For example, in Super Mario World, human players seem to look ahead in extended steps, such as climbing a vine or jumping over a pit, rather than planning one frame at a time. Currently though, this type of reasoning does not come easily to machines, leaving many open research problems related to task decomposition. This thesis focuses on three such problems in particular: (1) The challenge of learning subgoals autonomously, so as to lessen the issue of sparse feedback. (2) The challenge of combining discrete planning techniques with extended actions whose durations and effects on the environment are uncertain. (3) The questions of when and why it is beneficial to reason over high-level continuous control variables, such as the velocity of a player-controlled ship, rather than over the most low-level actions available. We address these problems via new algorithms and novel experimental design, demonstrating empirically that our algorithms are more efficient than strong baselines that do not leverage task decomposition, and yielding insight into the types of environment where task decomposition is likely to be beneficial

Learning the Structure of Continuous Markov Decision Processes

There is growing interest in artificial, intelligent agents which can operate autonomously for an extended period of time in complex environments and fulfill a variety of different tasks. Such agents will face different problems during their lifetime which may not be foreseeable at the time of their deployment. Thus, the capacity for lifelong learning of new behaviors is an essential prerequisite for this kind of agents as it enables them to deal with unforeseen situations. However, learning every complex behavior anew from scratch would be cumbersome for the agent. It is more plausible to consider behavior to be modular and let the agent acquire a set of reusable building blocks for behavior, the so-called skills. These skills might, once acquired, facilitate fast learning and adaptation of behavior to new situations. This work focuses on computational approaches for skill acquisition, namely which kind of skills shall be acquired and how to acquire them. The former is commonly denoted as skill discovery and the latter as skill learning . The main contribution of this thesis is a novel incremental skill acquisition approach which is suited for lifelong learning. In this approach, the agent learns incrementally a graph-based representation of a domain and exploits certain properties of this graph such as its bottlenecks for skill discovery. This thesis proposes a novel approach for learning a graph-based representation of continuous domains based on formalizing the problem as a probabilistic generative model. Furthermore, a new incremental agglomerative clustering approach for identifying bottlenecks of such graphs is presented. Thereupon, the thesis proposes a novel intrinsic motivation system which enables an agent to intelligently allocate time between skill discovery and skill learning in developmental settings, where the agent is not constrained by external tasks. The results of this thesis show that the resulting skill acquisition approach is suited for continuous domains and can deal with domain stochasticity and different explorative behavior of the agent. The acquired skills are reusable and versatile and can be used in multi-task and lifelong learning settings in high-dimensional problems

Learning Parameterized Skills

One of the defining characteristics of human intelligence is the ability to acquire and refine skills. Skills are behaviors for solving problems that an agent encounters often—sometimes in different contexts and situations—throughout its lifetime. Identifying important problems that recur and retaining their solutions as skills allows agents to more rapidly solve novel problems by adjusting and combining their existing skills. In this thesis we introduce a general framework for learning reusable parameterized skills. Reusable skills are parameterized procedures that—given a description of a problem to be solved—produce appropriate behaviors or policies. They can be sequentially and hierarchically combined with other skills to produce progressively more abstract and temporally extended behaviors. We identify three major challenges involved in the construction of such skills. First, an agent should be capable of solving a small number of problems and generalizing these experiences to construct a single reusable skill. The skill should be capable of producing appropriate behaviors even when applied to yet unseen variations of a problem. We introduce a method for estimating properties of the lower-dimensional manifold on which problem solutions lie. This allows for the construction of unified models for predicting policies from task parameters. Secondly, the agent should be able to identify when a skill can be hierarchically decomposed into specialized sub-skills. We observe that the policy manifold may be composed of disjoint, piecewise-smooth charts, each one encoding solutions for a subclass of problems. Identifying and modeling sub-skills allows for the aggregation of related behaviors into single, more abstract skills. Finally, the agent should be able to actively select on which problems to practice in order to more rapidly become competent in a skill. Thoughtful and deliberate practice is one of the defining characteristics of human expert performance. By carefully choosing on which problems to practice the agent might more rapidly construct a skill that performs well over a wide range of problems. We address these challenges via a general framework for skill acquisition. We evaluate it on simulated decision-problems and on a physical humanoid robot, and demonstrate that it allows for the efficient and active construction of reusable skills

Self Adaptive Reinforcement Learning for High-Dimensional Stochastic Systems with Application to Robotic Control

A long standing goal in the field of artificial intelligence (AI) is to develop agents that can perceive richer problem space and effortlessly plan their activity in minimal duration. Several strides have been made towards this goal over the last few years due to simultaneous advances in compute power, optimized algorithms, and most importantly evident success of AI based machines in nearly every discipline. The progress has been especially rapid in area of reinforcement learning (RL) where computers can now plan-ahead their activities and outperform their human rivals in complex problem domains like chess or Go game. However, despite encouraging progress, most of the advances in RL-based planning still take place in deterministic context (e.g. constant grid size, known action sets, etc.) which does not adapts well to stochastic variations in problem domain. In this dissertation we develop techniques that enable self-adaptation of agent\u27s behavioral policy when exposed to variations in problem domain. In particular, first we introduce an initial model that loosely realizes problem domain\u27s characteristics. The domain characteristics are embedded into a common multi-modal embedding space set. The embedding space set then allows us to identify initial beliefs and establish prior distributions without being constrained to only finite collection of agent\u27s state-action-reward experiences to choose from. We describe a learning technique that adapts to variations in problem domain by retaining only salient features of preceding domains, and inferring posterior for newly introduced variation as direct perturbation to aggregated priors. Besides having theoretical guarantees, we demonstrate end-to-end solution by establishing FPGA-based recurrent neural network, that can change its synaptic architecture temporally, thus eliminating the need of maintaining dual networks. We argue that our hardware based neural implementation has practical benefits, due to the fact it only uses sparse network architecture and multiplex it on circuit level to exhibit recurrence, which can reduce inference latency on circuit-level, while maintaining equivalence to dense neural architecture

Real-Time Hybrid Visual Servoing of a Redundant Manipulator via Deep Reinforcement Learning

Fixtureless assembly may be necessary in some manufacturing tasks and environ-ments due to various constraints but poses challenges for automation due to non-deterministic characteristics not favoured by traditional approaches to industrial au-tomation. Visual servoing methods of robotic control could be effective for sensitive manipulation tasks where the desired end-effector pose can be ascertained via visual cues. Visual data is complex and computationally expensive to process but deep reinforcement learning has shown promise for robotic control in vision-based manipu-lation tasks. However, these methods are rarely used in industry due to the resources and expertise required to develop application-specific systems and prohibitive train-ing costs. Training reinforcement learning models in simulated environments offers a number of benefits for the development of robust robotic control algorithms by reducing training time and costs, and providing repeatable benchmarks for which algorithms can be tested, developed and eventually deployed on real robotic control environments. In this work, we present a new simulated reinforcement learning envi-ronment for developing accurate robotic manipulation control systems in fixtureless environments. Our environment incorporates a contemporary collaborative industrial robot, the KUKA LBR iiwa, with the goal of positioning its end effector in a generic fixtureless environment based on a visual cue. Observational inputs are comprised of the robotic joint positions and velocities, as well as two cameras, whose positioning reflect hybrid visual servoing with one camera attached to the robotic end-effector, and another observing the workspace respectively. We propose a state-of-the-art deep reinforcement learning approach to solving the task environment and make prelimi-nary assessments of the efficacy of this approach to hybrid visual servoing methods for the defined problem environment. We also conduct a series of experiments ex-ploring the hyperparameter space in the proposed reinforcement learning method. Although we could not prove the efficacy of a deep reinforcement approach to solving the task environment with our initial results, we remain confident that such an ap-proach could be feasible to solving this industrial manufacturing challenge and that our contributions in this work in terms of the novel software provide a good basis for the exploration of reinforcement learning approaches to hybrid visual servoing in accurate manufacturing contexts

Advances in Reinforcement Learning

Reinforcement Learning (RL) is a very dynamic area in terms of theory and application. This book brings together many different aspects of the current research on several fields associated to RL which has been growing rapidly, producing a wide variety of learning algorithms for different applications. Based on 24 Chapters, it covers a very broad variety of topics in RL and their application in autonomous systems. A set of chapters in this book provide a general overview of RL while other chapters focus mostly on the applications of RL paradigms: Game Theory, Multi-Agent Theory, Robotic, Networking Technologies, Vehicular Navigation, Medicine and Industrial Logistic

Artificial general intelligence: Proceedings of the Second Conference on Artificial General Intelligence, AGI 2009, Arlington, Virginia, USA, March 6-9, 2009

Artificial General Intelligence (AGI) research focuses on the original and ultimate goal of AI – to create broad human-like and transhuman intelligence, by exploring all available paths, including theoretical and experimental computer science, cognitive science, neuroscience, and innovative interdisciplinary methodologies. Due to the difficulty of this task, for the last few decades the majority of AI researchers have focused on what has been called narrow AI – the production of AI systems displaying intelligence regarding specific, highly constrained tasks. In recent years, however, more and more researchers have recognized the necessity – and feasibility – of returning to the original goals of the field. Increasingly, there is a call for a transition back to confronting the more difficult issues of human level intelligence and more broadly artificial general intelligence

Deriving Subgoals Autonomously to Accelerate Learning in Sparse Reward Domains.

Sparse reward games, such as the infamous Montezumas Revenge, pose a significant challenge for Reinforcement Learning (RL) agents. Hierarchical RL, which promotes efficient exploration via subgoals, has shown promise in these games. However, existing agents rely either on human domain knowledge or slow autonomous methods to derive suitable subgoals. In this work, we describe a new, autonomous approach for deriving subgoals from raw pixels that is more efficient than competing methods. We propose a novel intrinsic reward scheme for exploiting the derived subgoals, applying it to three Atari games with sparse rewards. Our agents performance is comparable to that of state-of-the-art methods, demonstrating the usefulness of the subgoals found