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#reinforcement-learning
Question
What is the update rule (policy gradient) used for the Manager in FeUdal Networks?

$$\nabla g_{t}=A_{t}^{M} \nabla_{\theta} d_{\cos }\left(s_{t+c}-s_{t}, g_{t}(\theta)\right)$$

where $$A_{t}^{M}=R_{t}-V_{t}^{M}\left(x_{t}, \theta\right)$$ is the Manager’s advantage function, computed using a value function estimate $$V_{t}^{M}\left(x_{t}, \theta\right)$$ from the internal critic; $$d_{\cos }\left(\alpha, \beta\right) = \alpha^T\beta /\left(|\alpha||\beta|\right)$$ is the cosine similarity between two vectors.

Tags
#reinforcement-learning
Question
What is the update rule (policy gradient) used for the Manager in FeUdal Networks?
?

Tags
#reinforcement-learning
Question
What is the update rule (policy gradient) used for the Manager in FeUdal Networks?

$$\nabla g_{t}=A_{t}^{M} \nabla_{\theta} d_{\cos }\left(s_{t+c}-s_{t}, g_{t}(\theta)\right)$$

where $$A_{t}^{M}=R_{t}-V_{t}^{M}\left(x_{t}, \theta\right)$$ is the Manager’s advantage function, computed using a value function estimate $$V_{t}^{M}\left(x_{t}, \theta\right)$$ from the internal critic; $$d_{\cos }\left(\alpha, \beta\right) = \alpha^T\beta /\left(|\alpha||\beta|\right)$$ is the cosine similarity between two vectors.

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#### pdf

owner: reseal - (no access) - FeUdal Networks for Hierarchical Reinforcement Learning, p3

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