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Q-learning uses an exploratory policy, derived from the current estimate of the $Q$ function, such as the $\epsilon$-greedy policy, to select the action $a$ from the current state $s$. After having taken this action $a$ from $s$, the reward $r$ and the next state $s'$ are observed. At this point, to update the estimate of the $Q$ function, you use a target ...


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The usual (as presented in Reinforcement Learning: An Introduction) $Q$-learning and SARSA algorithms use (and update) a function of a state $s$ and action $a$, $Q(s, a)$. These algorithms assume that the current state $s$ is known. However, in POMDP, at each time step, the agent does not know the current state, but it maintains a "belief" (which, ...


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Why is the action selection random with Sarsa? A policy could be stochastic. In the case of SARSA, it is stochastic because of the use of $\epsilon$-greedy. Isn't it on-policy and therefore ϵ-greedy? I don't quite understand the question. SARSA is on-policy evaluation with $\epsilon$-greedy policy. Q-learning is off-policy evaluation with $\epsilon$-...


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The $\epsilon$-greedy policy is a policy that chooses the best action (i.e. the action associated with the highest value) with probability $1-\epsilon \in [0, 1]$ and a random action with probability $\epsilon $. The problem with $\epsilon$-greedy is that, when it chooses the random actions (i.e. with probability $\epsilon$), it chooses them uniformly (i.e. ...


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I think you can break this problem down into two parts to try and find the solution. 1. Can the neural network model the desired function? Take the tabular function you have learned in the exact agent, and treat it as training data for the neural network model, using the same loss function and other hyperparameters as you intend to use when the NN is being ...


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Multiplying the entire update by $\rho$ has the desirable property that experience affects $Q$ less when the behavior policy is unrelated to the target policy. In the extreme, if the trajectory taken has zero probability under the target policy, then $Q$ isn't updated at all, which is good. Alternatively, if only $G$ is scaled by $\rho$, taking zero ...


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