Q-Learning An agent tries an action at a particular state, and - PowerPoint PPT Presentation

Mar 10, 2024 •225 likes •290 views

Q-Learning An agent tries an action at a particular state, and evaluates its consequences in terms of the immediate reward or penalty it receives and its estimate of the value of the state to which it is taken. The paper shows that

Q-Learning • An agent tries an action at a particular state, and evaluates its consequences in terms of the immediate reward or penalty it receives and its estimate of the value of the state to which it is taken. • The paper shows that Q-learning converges to the optimum action-values with probability 1 so long as all actions are repeatedly sampled in all states and the action-values are represented discretely.
π(x) is the next state recommended by policy π Prob that y= π(x) The the value of state x is Instant reward of Value of new state y going to the state which policy π recommends Learning rate
The goal of Q-learning is to find π*, such that for any given state x , π* recommends the action a that will maximize the value of current state.
To get this optimal policy π*, we build the matrix Q in incremental way, like in Dynamic Programming: Now, since we want π(x) to be optimal, it will recommend max(V π (y)) with probability 1. So, equation becomes: Q(x, a) = R x (a) + γ * max a’ (Q(x’, a’))
Data Structures Matrix “R” is the reward matrix. R[x][a] denotes instant reward of • performing action a at state x . Only the actions leading to goal state have positive reward. Matrix “Q” is the brain matrix. It represents the memory of what our • agent has learned through experience. Q[x][a] denotes learned reward of performing action a at state x. Q can be initially zero. However, size of these matrices depends on the size of action and • state space, which could be exponential. So, we generally use look-up tables instead.
References • [1992] "Q-Learning". Christopher Watkins, Peter Dayan. Nature Publishing Group.

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