Reinforcement Learning Applied to a Game of Deceit
نویسنده
چکیده
Skull is a simple game of deception played by 3-6 players. Each player receives four tiles. Three of these tiles depict flowers, with the fourth depicting a skull. At the beginning of a round, all players simultaneously choose one of their tiles and places it face-down on the table. Then play proceeds clockwise, with each player taking one of two actions: Add or Bet. If a player chooses Add, they place another tile face-down on top of their stack. If they Bet, they choose a number and from then onward, each player has a choice of two actions: Raise or Pass. If a player Raises, their bet (higher than the previous bet) replaces the previous bet. If a player Passes, they are out of the round. Once all players but one have Passed, the player who made the last bet must turn over a number of tiles equal to their bet, starting with their own stack. If they turn over only flowers, they win 1 point. If they turn over a skull, they permanently lose one of their four discs (losing all four means that a player has lost the game). The first player to win 2 points wins the game. 2 Motivation
منابع مشابه
An Adaptive Learning Game for Autistic Children using Reinforcement Learning and Fuzzy Logic
This paper, presents an adapted serious game for rating social ability in children with autism spectrum disorder (ASD). The required measurements are obtained by challenges of the proposed serious game. The proposed serious game uses reinforcement learning concepts for being adaptive. It is based on fuzzy logic to evaluate the social ability level of the children with ASD. The game adapts itsel...
متن کاملDevelopment of Reinforcement Learning Algorithm to Study the Capacity Withholding in Electricity Energy Markets
This paper addresses the possibility of capacity withholding by energy producers, who seek to increase the market price and their own profits. The energy market is simulated as an iterative game, where each state game corresponds to an hourly energy auction with uniform pricing mechanism. The producers are modeled as agents that interact with their environment through reinforcement learning (RL...
متن کاملApplication of Stochastic Optimal Control, Game Theory and Information Fusion for Cyber Defense Modelling
The present paper addresses an effective cyber defense model by applying information fusion based game theoretical approaches. In the present paper, we are trying to improve previous models by applying stochastic optimal control and robust optimization techniques. Jump processes are applied to model different and complex situations in cyber games. Applying jump processes we propose some m...
متن کاملReinforcement Learning Based PID Control of Wind Energy Conversion Systems
In this paper an adaptive PID controller for Wind Energy Conversion Systems (WECS) has been developed. Theadaptation technique applied to this controller is based on Reinforcement Learning (RL) theory. Nonlinearcharacteristics of wind variations as plant input, wind turbine structure and generator operational behaviordemand for high quality adaptive controller to ensure both robust stability an...
متن کاملMulticast Routing in Wireless Sensor Networks: A Distributed Reinforcement Learning Approach
Wireless Sensor Networks (WSNs) are consist of independent distributed sensors with storing, processing, sensing and communication capabilities to monitor physical or environmental conditions. There are number of challenges in WSNs because of limitation of battery power, communications, computation and storage space. In the recent years, computational intelligence approaches such as evolutionar...
متن کامل