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See detailDeep Reinforcement Learning Solutions for Energy Microgrids Management
François-Lavet, Vincent ULg; Taralla, David; Ernst, Damien ULg et al

in European Workshop on Reinforcement Learning (EWRL 2016) (2016, December)

This paper addresses the problem of efficiently operating the storage devices in an electricity microgrid featuring photovoltaic (PV) panels with both short- and long-term storage capacities. The problem ... [more ▼]

This paper addresses the problem of efficiently operating the storage devices in an electricity microgrid featuring photovoltaic (PV) panels with both short- and long-term storage capacities. The problem of optimally activating the storage devices is formulated as a sequential decision making problem under uncertainty where, at every time-step, the uncertainty comes from the lack of knowledge about future electricity consumption and weather dependent PV production. This paper proposes to address this problem using deep reinforcement learning. To this purpose, a specific deep learning architecture has been designed in order to extract knowledge from past consumption and production time series as well as any available forecasts. The approach is empirically illustrated in the case of a residential customer located in Belgium. [less ▲]

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See detailDecision Making from Confidence Measurement on the Reward Growth using Supervised Learning: A Study Intended for Large-Scale Video Games
Taralla, David ULg; Qiu, Zixiao ULg; Sutera, Antonio ULg et al

in Proceedings of the 8th International Conference on Agents and Artificial Intelligence (ICAART 2016) - Volume 2 (2016, February)

Video games have become more and more complex over the past decades. Today, players wander in visually and option- rich environments, and each choice they make, at any given time, can have a combinatorial ... [more ▼]

Video games have become more and more complex over the past decades. Today, players wander in visually and option- rich environments, and each choice they make, at any given time, can have a combinatorial number of consequences. However, modern artificial intelligence is still usually hard-coded, and as the game environments become increasingly complex, this hard-coding becomes exponentially difficult. Recent research works started to let video game autonomous agents learn instead of being taught, which makes them more intelligent. This contribution falls under this very perspective, as it aims to develop a framework for the generic design of autonomous agents for large-scale video games. We consider a class of games for which expert knowledge is available to define a state quality function that gives how close an agent is from its objective. The decision making policy is based on a confidence measurement on the growth of the state quality function, computed by a supervised learning classification model. Additionally, no stratagems aiming to reduce the action space are used. As a proof of concept, we tested this simple approach on the collectible card game Hearthstone and obtained encouraging results. [less ▲]

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See detailLearning Artificial Intelligence in Large-Scale Video Games: A First Case Study with Hearthstone: Heroes of Warcraft
Taralla, David ULg

Master's dissertation (2015)

Over the past twenty years, video games have become more and more complex thanks to the emergence of new computing technologies. The challenges players face now involve the simultaneous consideration of ... [more ▼]

Over the past twenty years, video games have become more and more complex thanks to the emergence of new computing technologies. The challenges players face now involve the simultaneous consideration of many game environment variables — they usually wander in rich 3D environments and have the choice to take numerous actions at any time, and taking an action has combinatorial consequences. However, the artificial intelligence (AI) featured in those games is often not complex enough to feel natural (human). Today's AI is still most of the time hard-coded, but as the game environments become increasingly complex, this task becomes exponentially difficult. To circumvent this issue and come with rich autonomous agents in large-scale video games, many research works already tried and succeeded in making video game AI learn instead of being taught. This thesis does its bit towards this goal. In this work, supervised learning classification based on extremely randomized trees is attempted as a solution to the problem of selecting an action amongst the set of available ones in a given state. In particular, we place ourselves in the context where no assumptions are made on the kind of actions available and where action simulations are not possible to find out what consequences these have on the game. This approach is tested on the collectible card game Hearthstone: HoW, for which an easily-extensible simulator was built. Encouraging results were obtained when facing Nora, the resulting Mage agent, against random and scripted (medium-level) Mage players. Furthermore, besides quantitative results, a qualitative experiment showed that the agent successfully learned to exhibit a board control behavior without having been explicitly taught to do so. [less ▲]

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See detailA feature-based approach for best arm identification in the case of the Monte Carlo search algorithm discovery for one-player games
Taralla, David ULg

Report (2013)

The field of reinforcement learning recently received the contribution by Ernst et al. (2013) "Monte carlo search algorithm discovery for one player games" who introduced a new way to conceive completely ... [more ▼]

The field of reinforcement learning recently received the contribution by Ernst et al. (2013) "Monte carlo search algorithm discovery for one player games" who introduced a new way to conceive completely new algorithms. Moreover, it brought an automatic method to find the best algorithm to use in a particular situation using a multi-arm bandit approach. We address here the problem of best arm identification. The main problem is that the generated algorithm space (ie. the arm space) can be quite large as the depth of the generated algorithms increases, so we just can't sample each algorithm the right number of times to be confident enough on the final choice (ie., to be sure the regret is minimized). We need therefore an optimized, scalable method for selecting the best algorithm from bigger spaces. The main idea is to see the reward of pulling an arm as a function of its features rather than directly exploring the algorithm space to find the best arm. This way, we demonstrate we are able to design a confident best arm identification algorithm, without suffering from the size of the space. [less ▲]

Detailed reference viewed: 145 (25 ULg)