In calculating policy gradients, wouldn't longer trajectories have more weight according to formula?
$begingroup$
In Sergey Levine's lecture on policy gradients (berkeley deep rl course), he show that policy gradient can be evaluated according to the formula
In this formula, wouldn't longer trajectories get more weight (in finite horizon situations), since the middle term, the sum over log pi, would involve more terms? (Why would it work like that?)
The specific example I have in mind is pacman, longer trajectories would contribute more to the gradient. Should it work like that?
reinforcement-learning policy-gradients
asked 13 mins ago
liyuanliyuan
133
$endgroup$
add a comment |
$begingroup$
In Sergey Levine's lecture on policy gradients (berkeley deep rl course), he show that policy gradient can be evaluated according to the formula
In this formula, wouldn't longer trajectories get more weight (in finite horizon situations), since the middle term, the sum over log pi, would involve more terms? (Why would it work like that?)
The specific example I have in mind is pacman, longer trajectories would contribute more to the gradient. Should it work like that?
reinforcement-learning policy-gradients
asked 13 mins ago
liyuanliyuan
133
$endgroup$
add a comment |
$begingroup$
In Sergey Levine's lecture on policy gradients (berkeley deep rl course), he show that policy gradient can be evaluated according to the formula
In this formula, wouldn't longer trajectories get more weight (in finite horizon situations), since the middle term, the sum over log pi, would involve more terms? (Why would it work like that?)
The specific example I have in mind is pacman, longer trajectories would contribute more to the gradient. Should it work like that?
reinforcement-learning policy-gradients
asked 13 mins ago
liyuanliyuan
133
$endgroup$
In Sergey Levine's lecture on policy gradients (berkeley deep rl course), he show that policy gradient can be evaluated according to the formula
In this formula, wouldn't longer trajectories get more weight (in finite horizon situations), since the middle term, the sum over log pi, would involve more terms? (Why would it work like that?)
The specific example I have in mind is pacman, longer trajectories would contribute more to the gradient. Should it work like that?
reinforcement-learning policy-gradients
reinforcement-learning policy-gradients
asked 13 mins ago
liyuanliyuan
133
asked 13 mins ago
liyuanliyuan
133
asked 13 mins ago
liyuanliyuan
133
asked 13 mins ago
liyuanliyuan
133
asked 13 mins ago
liyuanliyuan
133
133
add a comment |
add a comment |
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