Comments (3)
Thanks for the issue. policy
can be any function that maps the state of the agent(sorn) to action parameterized by output_layer_weights. The given example has a stochastic policy. Extra care on next line, using random.choice
sampling action from the policy.
def policy(state,w):
"Implementation of softmax policy"
z = state.dot(w)
exp = np.exp(z)
return exp/np.sum(exp)
from sorn.
I have added the sample policy method in the readme. Thanks again. For further info, please feel free to create a new issue ;)
from sorn.
Thank you for the quick response @Saran-nns !
from sorn.
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