How can I use the success and failure data to estimate parameters of a Dirichlet distribution? I have used Beta function to estimate the performance of the agent. I have failure and success data of the task that runs on the agent. 
The parameter $\alpha$ is a number of successful tasks, while $\beta$ is the number of failures. Thus, I can estimate the performance by exploiting the expected value of Beta, as $$\mu = \frac{\alpha} {(\alpha+\beta)}$$

So, I am looking for a similar model, such that its parameter can be estimated from the success and failure data. So far I found Dirichlet distribution. 

What is the expected value of Dirichlet distribution? How I can use the success and failure data to estimate parameters of this distribution?

Let's check the following example:

Suppose that we use a Dirichlet prior represented by $Dirichlet(1, 1, 1)$ and observe $13$ results with $8$ Successful, $2$ Missing, and $3$ Failures. Then we get the posterior to be $Dirichlet(1+8, 1+2, 1+3)$. Then if you define the performance value $\alpha$ to be the expectation of $P(x=Successful)$, 
then $\alpha$ will be $(1+8)/[(1+8)+(1+2)+(1+3)] = 0.56$

Now 
Suppose that we use a Beta prior represented by $Beta(1,1)$ and observe $13$ results with $8$ Successful, and $3$ Failures. Then we get the posterior to be $Beta(1+8, 1+3)$. Then if you define the performance value Pr to be the expectation of $P(x=Successful)$, 
then $\alpha = (1+8)/[(1+8)+(1+3)] = 0.69$

Are my calculations and concept right?
Dirichlet is the Multi Variate version of the Beta distribution. In general, these distributions can be thought to model the probability of modelling a probability distribution. 

The support Dirichlet distribution is defined as follows:

$$ S_K = \{ x:0 \leq x_k \leq 1, \sum_{k=1}^K x_k=1 \} $$

and the PDF is defined as:

$$Dir(x|\alpha) = \frac{1}{B(\alpha)} \prod_{k=1}^Kx_k^{\alpha_k-1}$$

where $B(\alpha)$ is the beta function of $K$ variables:

$$B(\alpha) = \frac{\prod_{k=1}^K \tau(\alpha_k)}{\tau(\sum_{k=1}^K \alpha_k)}$$

and the resultant point estimates are (Define $\sum_{k=1}^K \alpha_k = \alpha_0)$:

$$\mu(x_k) = \frac{\alpha_k}{\alpha_0}$$
$$\sigma^2(x_k) = \frac{\alpha_k(\alpha_0-\alpha_k)}{\alpha_0^2(\alpha_0+1)}$$
$$mode[x_k] = \frac{\alpha_k-1}{\alpha_0-1}$$

Beta distribution is the special case where $k=2$

Clearly, when you run an experiment a large number of times, the success of each $k$ will approach towards its expected value i.e if you define your random variables as $x_k = \frac{N_{k}}{N}$ where $N$ is the total number of trials and $N_k$ is the success of the $k$ th term, it clearly satisfies the support of the Dirichlet Distribution and hence you can use 

$$\frac{\alpha_{k}}{\alpha_0} = \frac{N_{k}}{N}$$

This is assuming that the experiment follows Dirichlet Distribution.

(Taken in parts from A Probabilistic Approach to ML)