How does Q-Learning deal with mixed strategies?
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I'm trying to understand how Q-learning deals with games where the optimal policy is a mixed strategy. The Bellman equation says that you should choose $max_a(Q(s,a))$ but this implies a single unique action for each $s$. Is Q-learning just not appropriate if you believe that the problem has a mixed strategy?
machine-learning reinforcement-learning q-learning
asked Dec 20 '18 at 17:48
Thomas JohnsonThomas Johnson
310129
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$begingroup$
I'm trying to understand how Q-learning deals with games where the optimal policy is a mixed strategy. The Bellman equation says that you should choose $max_a(Q(s,a))$ but this implies a single unique action for each $s$. Is Q-learning just not appropriate if you believe that the problem has a mixed strategy?
machine-learning reinforcement-learning q-learning
asked Dec 20 '18 at 17:48
Thomas JohnsonThomas Johnson
310129
$endgroup$
bumped to the homepage by Community♦ 6 hours ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
$begingroup$
I'm trying to understand how Q-learning deals with games where the optimal policy is a mixed strategy. The Bellman equation says that you should choose $max_a(Q(s,a))$ but this implies a single unique action for each $s$. Is Q-learning just not appropriate if you believe that the problem has a mixed strategy?
machine-learning reinforcement-learning q-learning
asked Dec 20 '18 at 17:48
Thomas JohnsonThomas Johnson
310129
$endgroup$
I'm trying to understand how Q-learning deals with games where the optimal policy is a mixed strategy. The Bellman equation says that you should choose $max_a(Q(s,a))$ but this implies a single unique action for each $s$. Is Q-learning just not appropriate if you believe that the problem has a mixed strategy?
machine-learning reinforcement-learning q-learning
machine-learning reinforcement-learning q-learning
asked Dec 20 '18 at 17:48
Thomas JohnsonThomas Johnson
310129
asked Dec 20 '18 at 17:48
Thomas JohnsonThomas Johnson
310129
edited Dec 20 '18 at 19:37
Thomas Johnson
asked Dec 20 '18 at 17:48
Thomas JohnsonThomas Johnson
310129
asked Dec 20 '18 at 17:48
Thomas JohnsonThomas Johnson
310129
asked Dec 20 '18 at 17:48
Thomas JohnsonThomas Johnson
310129
310129
bumped to the homepage by Community♦ 6 hours ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
bumped to the homepage by Community♦ 6 hours ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
add a comment |
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One possibility is to use softmax and choose each action a randomly with probabiliy $p = frac{exp(Q(s,a))}{sum_a exp(Q(s,a))}$. I don't thinks it is still Q-learning though.
answered Dec 20 '18 at 22:29
Robin NicoleRobin Nicole
1416
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$begingroup$
One possibility is to use softmax and choose each action a randomly with probabiliy $p = frac{exp(Q(s,a))}{sum_a exp(Q(s,a))}$. I don't thinks it is still Q-learning though.
answered Dec 20 '18 at 22:29
Robin NicoleRobin Nicole
1416
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add a comment |
$begingroup$
One possibility is to use softmax and choose each action a randomly with probabiliy $p = frac{exp(Q(s,a))}{sum_a exp(Q(s,a))}$. I don't thinks it is still Q-learning though.
answered Dec 20 '18 at 22:29
Robin NicoleRobin Nicole
1416
$endgroup$
add a comment |
$begingroup$
One possibility is to use softmax and choose each action a randomly with probabiliy $p = frac{exp(Q(s,a))}{sum_a exp(Q(s,a))}$. I don't thinks it is still Q-learning though.
answered Dec 20 '18 at 22:29
Robin NicoleRobin Nicole
1416
$endgroup$
One possibility is to use softmax and choose each action a randomly with probabiliy $p = frac{exp(Q(s,a))}{sum_a exp(Q(s,a))}$. I don't thinks it is still Q-learning though.
answered Dec 20 '18 at 22:29
Robin NicoleRobin Nicole
1416
answered Dec 20 '18 at 22:29
Robin NicoleRobin Nicole
1416
answered Dec 20 '18 at 22:29
Robin NicoleRobin Nicole
1416
answered Dec 20 '18 at 22:29
Robin NicoleRobin Nicole
1416
1416
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