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Tags

#reinforcement-learning

Question

What is the update rule (policy gradient) used for the Manager in FeUdal Networks?

Answer

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

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?

Answer

?

Tags

#reinforcement-learning

Question

What is the update rule (policy gradient) used for the Manager in FeUdal Networks?

Answer

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

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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status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

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