i'm trying to solve a problem in which i need to carry out reinforcement learning with multiple simultaneous actions in continuous action space . i checked the multiagent structure; however, im trying to solve a problem in which there are difficulties to set up connection between the agents. for instance, they should take actions simultaneously so there is no way they can be aware of each other's actions. so i decided to go with the multivariate normal solution. has anybody tried that out ever?
first of all i have have difficulties finding the covariance matrix. since it has to be PSD so i decided to assume covariance is zero. something like:
covariance matrix = [[variance1 0][0 variance2]]
but its not everything. the agent doesn't seem to be learning. the problem to be solved by the agent is about resource allocation so the "mean" can not be negative then i decided to go with the "RELU" activation function for the neural network. surprisingly, mean is usually zero so as you can guess its updating the weights in a way to do nothing (negative mean). on the other hand, the variances are on the rise. Though i have checked it a million times there might be a flaw on the code of the environment there is no doubt. i just wanted to to make sure if its mathematically ok to go in this way ? i checked for papers and i found bunch of them but they don't seem to be enough. i would appreciate any guidance.