3
votes
Accepted
Do all expert trajectories have the same starting state in apprenticeship learning?
All right, I figured it out. trajectories need not have the same starting state because the distribution of $s_0$ is drawn from a distribution D (mentioned in the paper). Had been confused because ...
- 1,251
3
votes
In imitation learning, do you simply inject optimal tuples of experience $(s, a, r, s')$ into your experience replay buffer?
That seems to be functional.
That is a great approach, as long as you are using an off-policy algorithm (since the samples you are using to learn are not the policy currently being performed), like Q-...
- 906
2
votes
Accepted
What does the notation ${s'\sim T(s,a,\cdot)}$ mean?
The dot ($.$) at the end of $T(s,a,.)$ shows all possible states that we can go from state $S$ by doing action $a$. As you know there are some probabilities here for choosing those states, that the ...
- 1,751
1
vote
Accepted
What does the number of required expert demonstrations in Imitation Learning depend on?
The answer to your question can be found in the original paper that introduced the max-margin and projection imitation learning (IL) algorithms: Apprenticeship Learning via Inverse Reinforcement ...
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