Run this, but I think better results can be obtained by using ensemble. Now you can have a complicated single network to Optimise according to the unit it is controlling at that given moment. In the image below we can see there are 12 of actions for a single unit. every unit has different actions that it can take, each city can build new Each unit has dynamic number of moves i.e.There are so many things you can do that it’s hard to remember them all,Īlso the action taken at any given moment can affect the results at later stage in the game. Saying freeciv is vast is an understatement. Possibility of using continuous actions to be taken in this. Reasons for choosing it, it will be one of the most challenging learning environments. Freeciv being a video game operates in discrete spaces. Where multiple agents each good at a particular thing collaborate to achieve goals far superior to what human can, this We do not need a single agent that is good at everything, in fact the future will be filled with ensemble architecture This also happens to be a notoriously difficult gameįor AI to master due to its sparse reward structure. Montezuma’s Revenge is an Atari game with discrete action space. Which are affected due to poor network conditions, notification systems on social networking sites to notify you only Other applications are, optimising video quality in streaming services Of real world application in discrete spaces. Medicine, where we require roll out of actions over time is an example Idea of deep RL takes actions in discrete domain. So far most of the research that we have seen has been in this domain, DQN which revolutionised the Video games a great example of this, they need a very specific single action that needs to be taken atĪny given moment. Discrete action spaces have a different task, all those applications where hard decisions are to be made we have aĭiscrete space.Self driving car operates in the continuous distribution And our algorithms just aren’t good at sampleĮfficiency and training on real life robots would mean extreme long training durations making it unfeasible. Trained on robot simulator rarely gives same results in real life robot). Over time, though at a slower rate and the reason for that is the simulators are just not good enough (ironically agent This domain is still in its early stages, but the applications nonetheless will be far felt. Movement vectors used to operate them is a set of force values and directions. Most of the applications in real life domain that involve use of hardware (Robotics, Self Driving Cars, etc.) require aĭistribution of values to be generated.In layman terms discrete spaces are where hard decision need to be taken and continuous spaces a distribution is to be But why care about this project?Įvery action in our universe can be broken down into two types, a continuous action space and a discrete action space. I am not asking the very hard questions here, like why care about anything. Multiplayer Collaborative Idea: How freeciv support different kinds of interaction and what is so special about itĪnd in each of those points I will also put sub-points to make understanding easier.
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