In calculating policy gradients, wouldn't longer trajectories have more weight according to formula?Why do we normalize the discounted rewards when doing policy gradient reinforcement learning?Why are policy gradients on-policy?Policy Gradients vs Value function, when implemented via DQNConvergence of vanilla or natural policy gradients (e.g. REINFORCE)How an action gets selected in a Policy Gradient Method?Time horizon T in policy gradients (actor-critic)Policy Gradient Methods - ScoreFunction & Log(policy)Policy Gradients - gradient Log probabilities favor less likely actions?Stability of value function approximation in policy gradientsUnderstanding policy gradient theorem - What does it mean to take gradients of reward wrt policy parameters?
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In calculating policy gradients, wouldn't longer trajectories have more weight according to formula?
Why do we normalize the discounted rewards when doing policy gradient reinforcement learning?Why are policy gradients on-policy?Policy Gradients vs Value function, when implemented via DQNConvergence of vanilla or natural policy gradients (e.g. REINFORCE)How an action gets selected in a Policy Gradient Method?Time horizon T in policy gradients (actor-critic)Policy Gradient Methods - ScoreFunction & Log(policy)Policy Gradients - gradient Log probabilities favor less likely actions?Stability of value function approximation in policy gradientsUnderstanding policy gradient theorem - What does it mean to take gradients of reward wrt policy parameters?
$begingroup$
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 4 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 4 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 4 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 4 mins ago
liyuanliyuan
133
asked 4 mins ago
liyuanliyuan
133
asked 4 mins ago
liyuanliyuan
133
asked 4 mins ago
liyuanliyuan
133
asked 4 mins ago
liyuanliyuan
133
133
add a comment |
add a comment |
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