Developing a Multiarmed Bandit's Predictive Model
One of the simplest RL problems is called n-armed bandits. The thing is there are n-many slot machines but each has different fixed payout probability. The goal is to maximize the profit by always choosing the machine with the best payout.
As mentioned earlier, we will also see how to use policy gradient that produces explicit outputs. For our multiarmed bandits, we don't need to formalize these outputs on any particular state. To be simpler, we can design our network such that it will consist of just a set of weights that are corresponding to each of the possible arms to be pulled in the bandit. Then, we will represent how good an agent thinks to pull each arm to make maximum profit. A naive way is to initialize these weights to 1 so that the agent will be optimistic about each arm's potential reward.
To update the network, we can try choosing an arm with a greedy policy that we discussed earlier. Our policy is such that the agent receives...
