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When running the code on DMC, because the `actor_grad` is `dynamics`; therefore, `loss_policy` would be `-value_target`. `value_target` is not dependent on the actor's policy distribution, and so, `lo…
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Hi! I have a few more questions about the code that I don't quite get.
First, I was wondering what pybullet_envs is for. I installed the library but got errors when i tried to import it. I also do…
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Sir, thanks for your book and code. it is very nice. In chapter14 DDPG code, i can't understand the code about the actor policy gradient update. This is part of your relevant code
act…
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## 🐛 Bug
It seems like there's 2 near identical implementations of VPG, with only a small change in loss function
https://github.com/PyTorchLightning/pytorch-lightning-bolts/blob/master/pl_bolts/m…
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In the simple policy gradient implementation [here][simple_pg], all of the observations, actions, and rewards for one "epoch" (potentially consisting of multiple episodes) are gathered into the same l…
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I was guided by the donut, and I found you did a fantastic job, which was part of my assumption. I hope to make some innovations on the basis of your work, but due to the scarcity of computing resourc…
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Hey @CR-Gjx Thanks for providing this open source code. Very helpful to study and I love the idea of hierarchical reinforcement learning.
In the recent AlphaGo Zero paper and [Thinking Fast and Slo…
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I've noticed that the REINFORCE algorithm (aka policy gradient, without the Q function) is not listed in the list of agents, even the not-yet-implemented ones. I presume this was intentional? How co…
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This is not an issue of the code per se, but I am learning RL and am wondering how the policy gradient is calculated in `pg_reinforce.py`. In this line
```
self.cross_entropy_loss = tf.nn.sparse_so…