309 research outputs found

    A survey of random processes with reinforcement

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    The models surveyed include generalized P\'{o}lya urns, reinforced random walks, interacting urn models, and continuous reinforced processes. Emphasis is on methods and results, with sketches provided of some proofs. Applications are discussed in statistics, biology, economics and a number of other areas.Comment: Published at http://dx.doi.org/10.1214/07-PS094 in the Probability Surveys (http://www.i-journals.org/ps/) by the Institute of Mathematical Statistics (http://www.imstat.org

    Learning competitive equilibrium in laboratory exchange economies

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    A laboratory market for two goods is instituted to examine the hypothesis that individuals will eventually coordinate on the induced competitive equilibrium. The mechanism for exchange strongly restricts the space of agent actions, facilitating the identification of decision rules. Evidence for learning competitive equilibrium is mixed due to strong heterogeneity in decision making. Some subjects forego immediately available gains when they expect the market to move in a more favorable direction, a condition necessary for coordinating on the competitive outcome. However, a majority do not, and many are content to satisfice, though the means to do better was reasonably transparent

    BNAIC 2008:Proceedings of BNAIC 2008, the twentieth Belgian-Dutch Artificial Intelligence Conference

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    Tuna Fishery Policy Analysis by using Game Theory Approach (Case Study: Sendang Biru)

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    Indonesia is the third largest fish producer in the world, after Japan and China in 2010, according to Food and Agricultural Organization (FAO). As the biggest archipelagic country, Indonesian maritime is one of its largest GDP contributor. Within the last decade, the fish industry has developed so much which followed by the significant number of exploration and exploitation activities. The problem of uncontrolled exploitations is the low stock of fish and fishery profit in the long period of time. The most recent issue of the high fish exploitation problem is the tuna fishery in Sendang Biru village. According to the local government data, the number of tuna catched in 2005 to 2010 is increasing yearly but from 2010 to 2015 the number is declining, although the number of fishing boat is increasing from 2005 to 2015. This phenomenon happened becaused of the over-exploitation on tuna. This research is aimed to develop a sustainable policy for local government, fisherman, and fish trader in exploiting tuna in Sendang Biru, which combine system dynamics and cooperative game theory approach. The system dynamics approach is used to replicate the tuna fishery system behaviour in Sedang Biru. The output of this system dynamics approach is used as the payoff for the game theory approach. The best sustainable policy is local government need to set the local fishing ship limit around 200 units, fisherman has to set medium amount of fishing trip, and fish trader need to set high profit margin on trading tuna. In case, local government is not limiting the number of ship it is better for fisherman to set low amount of fishing trip

    Many-agent Reinforcement Learning

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    Multi-agent reinforcement learning (RL) solves the problem of how each agent should behave optimally in a stochastic environment in which multiple agents are learning simultaneously. It is an interdisciplinary domain with a long history that lies in the joint area of psychology, control theory, game theory, reinforcement learning, and deep learning. Following the remarkable success of the AlphaGO series in single-agent RL, 2019 was a booming year that witnessed significant advances in multi-agent RL techniques; impressive breakthroughs have been made on developing AIs that outperform humans on many challenging tasks, especially multi-player video games. Nonetheless, one of the key challenges of multi-agent RL techniques is the scalability; it is still non-trivial to design efficient learning algorithms that can solve tasks including far more than two agents (N2N \gg 2), which I name by \emph{many-agent reinforcement learning} (MARL\footnote{I use the world of ``MARL" to denote multi-agent reinforcement learning with a particular focus on the cases of many agents; otherwise, it is denoted as ``Multi-Agent RL" by default.}) problems. In this thesis, I contribute to tackling MARL problems from four aspects. Firstly, I offer a self-contained overview of multi-agent RL techniques from a game-theoretical perspective. This overview fills the research gap that most of the existing work either fails to cover the recent advances since 2010 or does not pay adequate attention to game theory, which I believe is the cornerstone to solving many-agent learning problems. Secondly, I develop a tractable policy evaluation algorithm -- αα\alpha^\alpha-Rank -- in many-agent systems. The critical advantage of αα\alpha^\alpha-Rank is that it can compute the solution concept of α\alpha-Rank tractably in multi-player general-sum games with no need to store the entire pay-off matrix. This is in contrast to classic solution concepts such as Nash equilibrium which is known to be PPADPPAD-hard in even two-player cases. αα\alpha^\alpha-Rank allows us, for the first time, to practically conduct large-scale multi-agent evaluations. Thirdly, I introduce a scalable policy learning algorithm -- mean-field MARL -- in many-agent systems. The mean-field MARL method takes advantage of the mean-field approximation from physics, and it is the first provably convergent algorithm that tries to break the curse of dimensionality for MARL tasks. With the proposed algorithm, I report the first result of solving the Ising model and multi-agent battle games through a MARL approach. Fourthly, I investigate the many-agent learning problem in open-ended meta-games (i.e., the game of a game in the policy space). Specifically, I focus on modelling the behavioural diversity in meta-games, and developing algorithms that guarantee to enlarge diversity during training. The proposed metric based on determinantal point processes serves as the first mathematically rigorous definition for diversity. Importantly, the diversity-aware learning algorithms beat the existing state-of-the-art game solvers in terms of exploitability by a large margin. On top of the algorithmic developments, I also contribute two real-world applications of MARL techniques. Specifically, I demonstrate the great potential of applying MARL to study the emergent population dynamics in nature, and model diverse and realistic interactions in autonomous driving. Both applications embody the prospect that MARL techniques could achieve huge impacts in the real physical world, outside of purely video games
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