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Adaptive value function approximation in reinforcement learning using waveletsMitchley, Michael January 2016 (has links)
A thesis submitted to the Faculty of Science, School of Computational and Applied Mathematics University of the Witwatersrand, Johannesburg, in fulfilment of the requirements for the degree of Doctor of Philosophy. Johannesburg, South Africa, July 2015. / Reinforcement learning agents solve tasks by finding policies that maximise their reward
over time. The policy can be found from the value function, which represents the value
of each stateaction pair. In continuous state spaces, the value function must be approximated.
Often, this is done using a fixed linear combination of functions across all
dimensions.
We introduce and demonstrate the wavelet basis for reinforcement learning, a basis
function scheme competitive against state of the art fixed bases. We extend two online
adaptive tiling schemes to wavelet functions and show their performance improvement
across standard domains. Finally we introduce the Multiscale Adaptive Wavelet Basis
(MAWB), a waveletbased adaptive basis scheme which is dimensionally scalable and insensitive
to the initial level of detail. This scheme adaptively grows the basis function
set by combining across dimensions, or splitting within a dimension those candidate functions
which have a high estimated projection onto the Bellman error. A number of novel
measures are used to find this estimate.
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Reinforcement learning in commercial computer gamesCoggan, Melanie. January 2008 (has links)
No description available.

13 
Statesimilarity metrics for continuous Markov decision processesFerns, Norman Francis January 2007 (has links)
No description available.

14 
Discovering hierarchy in reinforcement learningHengst, Bernhard, Computer Science & Engineering, Faculty of Engineering, UNSW January 2003 (has links)
This thesis addresses the open problem of automatically discovering hierarchical structure in reinforcement learning. Current algorithms for reinforcement learning fail to scale as problems become more complex. Many complex environments empirically exhibit hierarchy and can be modeled as interrelated subsystems, each in turn with hierarchic structure. Subsystems are often repetitive in time and space, meaning that they reoccur as components of different tasks or occur multiple times in different circumstances in the environment. A learning agent may sometimes scale to larger problems if it successfully exploits this repetition. Evidence suggests that a bottom up approach that repetitively finds buildingblocks at one level of abstraction and uses them as background knowledge at the next level of abstraction, makes learning in many complex environments tractable. An algorithm, called HEXQ, is described that automatically decomposes and solves a multidimensional Markov decision problem (MDP) by constructing a multilevel hierarchy of interlinked subtasks without being given the model beforehand. The effectiveness and efficiency of the HEXQ decomposition depends largely on the choice of representation in terms of the variables, their temporal relationship and whether the problem exhibits a type of constrained stochasticity. The algorithm is first developed for stochastic shortest path problems and then extended to infinite horizon problems. The operation of the algorithm is demonstrated using a number of examples including a taxi domain, various navigation tasks, the Towers of Hanoi and a larger sporting problem. The main contributions of the thesis are the automation of (1)decomposition, (2) subgoal identification, and (3) discovery of hierarchical structure for MDPs with states described by a number of variables or features. It points the way to further scaling opportunities that encompass approximations, partial observability, selective perception, relational representations and planning. The longer term research aim is to train rather than program intelligent agents

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QLearning for Robot ControlGaskett, Chris, cgaskett@it.jcu.edu.au January 2002 (has links)
QLearning is a method for solving reinforcement learning problems. Reinforcement learning problems require improvement of behaviour based on received rewards. QLearning has the potential to reduce robot programming effort and increase the range of robot abilities. However, most currentQlearning systems are not suitable for robotics problems: they treat continuous variables, for example speeds or positions, as discretised values. Discretisation does not allow smooth control and does not fully exploit sensed information. A practical algorithm must also cope with realtime constraints, sensing and actuation delays, and incorrect sensor data.
This research describes an algorithm that deals with continuous state and action variables without discretising. The algorithm is evaluated with visionbased mobile robot and active head gaze control tasks. As well as learning the basic control tasks, the algorithm learns to compensate for delays in sensing and actuation by predicting the behaviour of its environment. Although the learned dynamic model is implicit in the controller, it is possible to extract some aspects of the model. The extracted models are compared to theoretically derived models of environment behaviour.
The difficulty of working with robots motivates development of methods that reduce experimentation time. This research exploits Qlearnings ability to learn by passively observing the robots actionsrather than necessarily controlling the robot. This is a valuable tool for shortening the duration of learning experiments.

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A study of modelbased average reward reinforcement learningOk, DoKyeong 09 May 1996 (has links)
Reinforcement Learning (RL) is the study of learning agents that improve
their performance from rewards and punishments. Most reinforcement learning
methods optimize the discounted total reward received by an agent, while, in many
domains, the natural criterion is to optimize the average reward per time step. In this
thesis, we introduce a modelbased average reward reinforcement learning method
called "Hlearning" and show that it performs better than other average reward and
discounted RL methods in the domain of scheduling a simulated Automatic Guided
Vehicle (AGV).
We also introduce a version of Hlearning which automatically explores the
unexplored parts of the state space, while always choosing an apparently best action
with respect to the current value function. We show that this "Autoexploratory HLearning"
performs much better than the original Hlearning under many previously
studied exploration strategies.
To scale Hlearning to large state spaces, we extend it to learn action models
and reward functions in the form of Bayesian networks, and approximate its value
function using local linear regression. We show that both of these extensions are very
effective in significantly reducing the space requirement of Hlearning, and in making
it converge much faster in the AGV scheduling task. Further, Autoexploratory Hlearning
synergistically combines with Bayesian network model learning and value
function approximation by local linear regression, yielding a highly effective average
reward RL algorithm.
We believe that the algorithms presented here have the potential to scale to
large applications in the context of average reward optimization. / Graduation date:1996

17 
Scaling multiagent reinforcement learning /Proper, Scott. January 1900 (has links)
Thesis (Ph. D.)Oregon State University, 2010. / Printout. Includes bibliographical references (leaves 121123). Also available on the World Wide Web.

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Structured exploration for reinforcement learningJong, Nicholas K. 18 December 2012 (has links)
Reinforcement Learning (RL) offers a promising approach towards achieving the dream of autonomous agents that can behave intelligently in the real world. Instead of requiring humans to determine the correct behaviors or sufficient knowledge in advance, RL algorithms allow an agent to acquire the necessary knowledge through direct experience with its environment. Early algorithms guaranteed convergence to optimal behaviors in limited domains, giving hope that simple, universal mechanisms would allow learning agents to succeed at solving a wide variety of complex problems. In practice, the field of RL has struggled to apply these techniques successfully to the full breadth and depth of realworld domains.
This thesis extends the reach of RL techniques by demonstrating the synergies among certain key developments in the literature. The first of these developments is modelbased exploration, which facilitates theoretical convergence guarantees in finite problems by explicitly reasoning about an agent's certainty in its understanding of its environment. A second branch of research studies function approximation, which generalizes RL to infinite problems by artificially limiting the degrees of freedom in an agent's representation of its environment. The final major advance that this thesis incorporates is hierarchical decomposition, which seeks to improve the efficiency of learning by endowing an agent's knowledge and behavior with the gross structure of its environment.
Each of these ideas has intuitive appeal and sustains substantial independent research efforts, but this thesis defines the first RL agent that combines all their benefits in the general case. In showing how to combine these techniques effectively, this thesis investigates the twin issues of generalization and exploration, which lie at the heart of efficient learning. This thesis thus lays the groundwork for the next generation of RL algorithms, which will allow scientific agents to know when it suffices to estimate a plan from current data and when to accept the potential cost of running an experiment to gather new data. / text

19 
Reinforcement learning in commercial computer gamesCoggan, Melanie. January 2008 (has links)
The goal of this thesis is to explore the use of reinforcement learning (RL) in commercial computer games. Although RL has been applied with success to many types of board games and nongame simulated environments, there has been little work in applying RL to the most popular genres of games: firstperson shooters, roleplaying games, and realtime strategies. In this thesis we use a firstperson shooter environment to create computer players, or bots, that learn to play the game using reinforcement learning techniques. / We have created three experimental bots: ChaserBot, ItemBot and HybridBot. The two first bots each focus on a different aspect of the firstperson shooter genre, and learn using basic RL. ChaserBot learns to chase down and shoot an enemy player. ItemBot, on the other hand, learns how to pick up the itemsweapons, ammunition, armorthat are available, scattered on the ground, for the players to improve their arsenal. Both of these bots become reasonably proficient at their assigned task. Our goal for the third bot, HybridBot, was to create a bot that both chases and shoots an enemy player and goes after the items in the environment. Unlike the two previous bots which only have primitive actions available (strafing right or left, moving forward or backward, etc.), HybridBot uses options. At any state, it may choose either the player chasing option or the item gathering option. These options' internal policies are determined by the data learned by ChaserBot and ItemBot. HybridBot uses reinforcement learning to learn which option to pick at a given state. / Each bot learns to perform its given tasks. We compare the three bots' ability to gather items, and ChaserBot's and HybridBot's ability to chase their opponent. HybridBot's results are of particular interest as it outperforms ItemBot at picking up items by a large amount. However, none of our experiments yielded bots that are competitive with human players. We discuss the reasons for this and suggest improvements for future work that could lead to competitive reinforcement learning bots.

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Statesimilarity metrics for continuous Markov decision processesFerns, Norman Francis. January 2007 (has links)
In recent years, various metrics have been developed for measuring the similarity of states in probabilistic transition systems (Desharnais et al., 1999; van Breugel & Worrell, 2001a). In the context of Markov decision processes, we have devised metrics providing a robust quantitative analogue of bisimulation. Most importantly, the metric distances can be used to bound the differences in the optimal value function that is integral to reinforcement learning (Ferns et al. 2004; 2005). More recently, we have discovered an efficient algorithm to calculate distances in the case of finite systems (Ferns et al., 2006). In this thesis, we seek to properly extend statesimilarity metrics to Markov decision processes with continuous state spaces both in theory and in practice. In particular, we provide the first distanceestimation scheme for metrics based on bisimulation for continuous probabilistic transition systems. Our work, based on statistical sampling and infinite dimensional linear programming, is a crucial first step in realworld planning; many practical problems are continuous in nature, e.g. robot navigation, and often a parametric model or crude finite approximation does not suffice. Statesimilarity metrics allow us to reason about the quality of replacing one model with another. In practice, they can be used directly to aggregate states.

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