19,586 research outputs found
The Hanabi Challenge: A New Frontier for AI Research
From the early days of computing, games have been important testbeds for
studying how well machines can do sophisticated decision making. In recent
years, machine learning has made dramatic advances with artificial agents
reaching superhuman performance in challenge domains like Go, Atari, and some
variants of poker. As with their predecessors of chess, checkers, and
backgammon, these game domains have driven research by providing sophisticated
yet well-defined challenges for artificial intelligence practitioners. We
continue this tradition by proposing the game of Hanabi as a new challenge
domain with novel problems that arise from its combination of purely
cooperative gameplay with two to five players and imperfect information. In
particular, we argue that Hanabi elevates reasoning about the beliefs and
intentions of other agents to the foreground. We believe developing novel
techniques for such theory of mind reasoning will not only be crucial for
success in Hanabi, but also in broader collaborative efforts, especially those
with human partners. To facilitate future research, we introduce the
open-source Hanabi Learning Environment, propose an experimental framework for
the research community to evaluate algorithmic advances, and assess the
performance of current state-of-the-art techniques.Comment: 32 pages, 5 figures, In Press (Artificial Intelligence
ViZDoom Competitions: Playing Doom from Pixels
This paper presents the first two editions of Visual Doom AI Competition,
held in 2016 and 2017. The challenge was to create bots that compete in a
multi-player deathmatch in a first-person shooter (FPS) game, Doom. The bots
had to make their decisions based solely on visual information, i.e., a raw
screen buffer. To play well, the bots needed to understand their surroundings,
navigate, explore, and handle the opponents at the same time. These aspects,
together with the competitive multi-agent aspect of the game, make the
competition a unique platform for evaluating the state of the art reinforcement
learning algorithms. The paper discusses the rules, solutions, results, and
statistics that give insight into the agents' behaviors. Best-performing agents
are described in more detail. The results of the competition lead to the
conclusion that, although reinforcement learning can produce capable Doom bots,
they still are not yet able to successfully compete against humans in this
game. The paper also revisits the ViZDoom environment, which is a flexible,
easy to use, and efficient 3D platform for research for vision-based
reinforcement learning, based on a well-recognized first-person perspective
game Doom
Poker Learner: Reinforcement Learning Applied to Texas Hold'em Poker
Bibliografia: p. 61-66Tese de Mestrado Integrado. Engenharia Informática e Computação. Universidade do Porto. Faculdade de Engenharia.. 201
Opponent Modelling in Multi-Agent Systems
Reinforcement Learning (RL) formalises a problem where an intelligent agent needs to learn and achieve certain goals by maximising a long-term return in an environment. Multi-agent reinforcement learning (MARL) extends traditional RL to multiple agents. Many RL algorithms lose convergence guarantee in non-stationary environments due to the adaptive opponents. Partial observation caused by agents’ different private observations introduces high variance during the training which exacerbates the data inefficiency. In MARL, training an agent to perform well against a set of opponents often leads to bad performance against another set of opponents. Non-stationarity, partial observation and unclear learning objective are three critical problems in MARL which hinder agents’ learning and they all share a cause which is the lack of knowledge of the other agents. Therefore, in this thesis, we propose to solve these problems with opponent modelling methods. We tailor our solutions by combining opponent modelling with other techniques according to the characteristics of problems we face. Specifically, we first propose ROMMEO, an algorithm inspired by Bayesian inference, as a solution to alleviate the non-stationarity in cooperative games. Then we study the partial observation problem caused by agents’ private observation and design an implicit communication training method named PBL. Lastly, we investigate solutions to the non-stationarity and unclear learning objective problems in zero-sum games. We propose a solution named EPSOM which aims for finding safe exploitation strategies to play against non-stationary opponents. We verify our proposed methods by varied experiments and show they can achieve the desired performance. Limitations and future works are discussed in the last chapter of this thesis
- …