![fear game controls fear game controls](https://pardonthegamer.files.wordpress.com/2018/03/4-noscale.jpg)
In our framework, different agents are concerned with different parts of the task. We presented initial work on a framework for solving single-agent tasks using multiple agents. In a paper, published prior to acquisition, “ Improving Scalability of Reinforcement Learning by Separation of Concerns”: Maluuba ( Recently acquired by Microsoft ) has also had active research. Very interesting that the axis are marked “Greed” and “Fear”, what better motivators are there anyway?ĭeepMind isn’t alone in its research of Multi-agent systems and Deep Learning. The paper has a very interesting chart the maps out the agent’s behavior ( “Gathering” on the left and “Wolfpack” on the right ):
![fear game controls fear game controls](https://i.ytimg.com/vi/DTpVflop_Ww/hqdefault.jpg)
discount factor, batch size, network size) that can be tweaked to arrive at different network behaviors. The primary value of the research is that it gives us an understanding of the many knobs (i.e. In the “Wolf Pack” game that was designed to encourage cooperative behavior, agents learning complex strategies did not necessarily led to greater cooperative behavior. In the “Gathering” game, when scarcity was introduced into the environment, agents with complex strategies tended to pursue more aggressive competitive strategies. The agents would have to learn either a cooperative or competitive strategy. Hence, such models give us the unique ability to test policies and interventions into simulated systems of interacting agents - both human and artificial.ĭeepMind researchers explored two games, “Gathering” and “Wolf Pack”. We can think of the trained AI agents as an approximation to economics’ rational agent model “homo economicus”. The DeepMind paper studies multi-agent systems from a similar economic perspective (i.e. It turns out that, DeepMind has been researching in this approach for a while. In another story, I address this by proposing the the use of market driven mechanisms as a means of control (see: “ Equilibrium Discovery in Modular Deep Learning”).
#FEAR GAME CONTROLS HOW TO#
The core problem of a multi-agent approach is how to control its behavior. This level is very similar to the “theory of mind” where we actually have multiple agent neural networks combining to solve problems.Īs we see from the above classification, the most advanced kind of Deep Learning system will involve multiple neural networks that either cooperate or compete to solve problems. Collaborative Classification with Imperfect Knowledge (CCIK) Classification with Imperfect Knowledge (CIK)Īt this level, we have a system that is built on top of CK, however is able to reason with imperfect information.ĥ. This level is somewhat similar to the CM level, however rather than raw memory, the information that the C level network is able to access is a symbolic knowledge base.Ĥ. This level includes memory elements incorporated with the C level networks. This level includes the fully connected neural network (FCN) and the convolution network (CNN) and various combinations of them. For discussion sake, I re-summarize it here again: In a previous story (see: “ Five Capability Levels of Deep Learning”, I laid out a road map as to how Deep Learning will evolve in even greater capabilities. The motivation is to study multi-agent systems to better understand and control these kinds of systems. The gist of the research is that, they employed Deep Reinforcement Learning networks in two game environments to study their behavior. Yesterday, February 19th 2017), DeepMind presents their latest research on this subject titled “ Understanding Agent Cooperation”. In this story, I now write about DeepMind’s latest foray into this exciting area. In a previous story, I wrote about how a Game Theoretic approach was influencing developments in the Deep Learning field.