pytorch-ddpg | Deep Deterministic Policy Gradient using PyTorch | Reinforcement Learning library
kandi X-RAY | pytorch-ddpg Summary
kandi X-RAY | pytorch-ddpg Summary
Implementation of the Deep Deterministic Policy Gradient (DDPG) using PyTorch
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Top functions reviewed by kandi - BETA
- Train an agent
- Save the model
- Observe current state
- Generate a random action
- Evaluate the given model
- Load weights
- Evaluate the model
- Finalize an episode
- Append an observation
- Get the output folder for a given environment
- Get the most recent observation
- Sample from the model
- Sets up a tensor
pytorch-ddpg Key Features
pytorch-ddpg Examples and Code Snippets
Community Discussions
Trending Discussions on pytorch-ddpg
QUESTION
I have an Actor Critic neural network where the Actor is its own class and the Critic is its own class with its own neural network and .forward() function. I then am creating an object of each of these classes in a larger Model class. My setup is as follows:
...ANSWER
Answered 2021-Jan-20 at 19:09Yes, you shouldn't do it like that. What you should do instead, is propagating through parts of the graph.
What the graph containsNow, graph contains both actor
and critic
. If the computations pass through the same part of graph (say, twice through actor), it will raise this error.
And they will, as you clearly use
actor
andcritic
joined with loss value (this line:loss_actor = -self.critic(state, action)
)Different optimizers do not change anything here, as it's
backward
problem (optimizers simply apply calculated gradients onto models)
- This is how to fix it in GANs, but not in this case, see
Actual fix
paragraph below, read on if you are curious about the topic
If part of a neural network (critic
in this case) does not take part in the current optimization step, it should be treated as a constant (and vice versa).
To do that, you could disable gradient
using torch.no_grad
context manager (documentation) and set critic
to eval
mode (documentation), something along those lines:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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Install pytorch-ddpg
You can use pytorch-ddpg like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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