Commit 2ac2b97f by Ruizhi Chen

修改公式1,2

parent 916cb6a8
\section{ Symbolic Language Producing} \section{ Symbolic Language Producing}
\label{sec:thory} \label{sec:thory}
...@@ -121,10 +120,10 @@ use the predicted result $\hat{t}$ of the listener agent as the ...@@ -121,10 +120,10 @@ use the predicted result $\hat{t}$ of the listener agent as the
evidence of whether giving positive rewards. Then, the gradients of the evidence of whether giving positive rewards. Then, the gradients of the
expected reward $ J(\theta_S, \theta_L)$ can be calculated as follows: expected reward $ J(\theta_S, \theta_L)$ can be calculated as follows:
\begin{align} \begin{align}
\nabla_{\theta^S} J &= \mathbb{E}_{\pi^S, \pi^L} \left[ r(\hat{t}, t) \cdot \nabla_{\theta^S} J &= \mathbb{E}_{\pi^S_{old}, \pi^L} \left[ r(\hat{t}, t) \cdot
\nabla_{\theta^S} \log{\pi^S(s_0, s_1 | t)} \right] \\ \frac{\nabla_{\theta^S}\pi^S(s_0, s_1 | t)}{\pi^S_{old}(s_0, s_1 | t)} \right] \\
\nabla_{\theta^L} J &= \mathbb{E}_{\pi^S, \pi^L} \left[ r(\hat{t}, t) \cdot \nabla_{\theta^L} J &= \mathbb{E}_{\pi^S, \pi^L_{old}} \left[ r(\hat{t}, t) \cdot
\nabla_{\theta^L} \log{\pi^S(\hat{t} | s_0, s_1)} \right] \frac{\nabla_{\theta^L} \pi^L(\hat{t} | s_0, s_1)}{\pi^L_{old}(\hat{t} | s_0, s_1)} \right]
\end{align} \end{align}
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