Exploding entropy temperature about pytorch-soft-actor-critic HOT 10 OPEN

I find it can be solved by delete "self.action_scale" in line 103 of model.py.
In SAC paper, this item does not exist. But I'm not sure. Maybe I miss something

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I am facing the same problem. It is not solved yet, however, I can locate the issue:

In GaussianPolicy -> sample(): The policy output x_t is transformed by tanh(x_t). If you look at the shape of tanh() then you will find out that this is equal to 1 or -1 for most of the arguments besides a small area between -5 and 5. As a result, we receive a clipped y_t. The actions are "fixed" to the action_space constraints. The algorithm stops exploring and is therefore increasing the temperature-factor alpha. This leads firstly to an exploding temperature factor and secondly to an exploding critic loss.

The solution should be to replace action by x_t in the return statement of the sample() method. However, this is leading to an error (underneath):

I am trying to solve this and let you know if there are updates from my side. If you have any thoughts or inputs on this, please let me know.

Traceback (most recent call last):
File "/Users/hammlerp/PycharmProjects/SupplyChainOptimization/src/Optimization/SoftActorCritic/sac_main.py", line 94, in
action = agent.select_action(state) # Sample action from policy
File "/Users/hammlerp/PycharmProjects/SupplyChainOptimization/src/Optimization/SoftActorCritic/sac.py", line 48, in select_action
action, _, _ = self.policy.sample(state)
File "/Users/hammlerp/PycharmProjects/SupplyChainOptimization/src/Optimization/SoftActorCritic/model.py", line 98, in sample
normal = Normal(mean, std)
File "/Users/hammlerp/opt/anaconda3/envs/SupplyChainOptimization/lib/python3.8/site-packages/torch/distributions/normal.py", line 50, in init
super(Normal, self).init(batch_shape, validate_args=validate_args)
File "/Users/hammlerp/opt/anaconda3/envs/SupplyChainOptimization/lib/python3.8/site-packages/torch/distributions/distribution.py", line 53, in init
raise ValueError("The parameter {} has invalid values".format(param))
ValueError: The parameter loc has invalid values

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So the code is working on your side with the learned temperature and w/o modification ?

Yes. It works without modification. If I have time in the evening I'll check LunarLander for you.

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Did you ever manage to solve this problem? I'm encountering similar exploding temperature for my environment no matter what value I choose, eventually the policy reaches that entropy, and temperature starts increasing...

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Hi, not at the moment. I also compared the implementation to OpenAI's baselines and experimented with theirs but with similar results. Working on multiple things at the moment but will provide an update if I find anything.

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this is equal to 1 or -1 for most of the arguments besides a small area between -5 and 5

Can you explain a little bit more what you meant by that ? I am also trying to fix it on my side

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Hi, not at the moment. I also compared the implementation to OpenAI's baselines and experimented with theirs but with similar results. Working on multiple things at the moment but will provide an update if I find anything.

The OpenAI implementation have also the temperature exploding ?

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this is equal to 1 or -1 for most of the arguments besides a small area between -5 and 5

Can you explain a little bit more what you meant by that ? I am also trying to fix it on my side.

The issue is solved on my side. Are you using a custom environment? My problem was related to the environment. Make sure to punish your agent when it proposes a value outside the desired interval.

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this is equal to 1 or -1 for most of the arguments besides a small area between -5 and 5

Can you explain a little bit more what you meant by that ? I am also trying to fix it on my side.

The issue is solved on my side. Are you using a custom environment? My problem was related to the environment. Make sure to punish your agent when it proposes a value outside the desired interval.

I am confused, I thought that the aim of the squashed guaussian was to not go outside of the interval ? So the code is working on your side with the learned temperature and w/o modification ?

(I am trying to use it on the 'LunarLanderContinuous-v2' right now, the scores hover around 0 and to solve it you need to have >200)

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Oh, my bad, it is working 😶 Thank you though :)

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