Comments (3)
Yes it should, but as it's not studied in this paper, I can't say if the overestimation is significant or if it impacts the performance of the algorithm, especially since it uses n-step estimates.
from td3.
I am confused about why the value of underestimated actions will not be explicitly propagated
through the policy update?
from td3.
Informally, the policy update pushes the policy to avoid underestimated actions.
from td3.
Related Issues (20)
- Huge overestimation of the Q values and lack of convergence HOT 1
- How to test model after training HOT 1
- policy update freq in DDPG & OurDDPG HOT 1
- SyntaxError HOT 1
- Minimum and Array-like Actions HOT 2
- Worry about the convergence proof HOT 1
- TD3 plotting details HOT 1
- Hi, i made re-implementation using yours but it doesn't work. HOT 1
- Figure explanation HOT 1
- ourddpg performance not good HOT 4
- question about random seeds HOT 3
- dockefile HOT 2
- Curve smoothing!
- Done state is set to 0 if episode timesteps are greater than or equal to max episode timesteps HOT 1
- argparse default type HOT 1
- what should be target_policy_noise target_noise_clip? HOT 1
- Where did -self.critic.Q1 come from? HOT 1
- Unable to generate results HOT 1
- Plots or graphs
- 请求许可使用该代码用于个人缩写的书中
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