Add a risk measure

Training a risk-averse model

SDDP.jl supports a variety of risk measures. Two common ones are SDDP.Expectation and SDDP.WorstCase. Let's see how to train a policy using them. There are three possible ways.

If the same risk measure is used at every node in the policy graph, we can just pass an instance of one of the risk measures to the risk_measure keyword argument of the SDDP.train function.

SDDP.train(
    model,
    risk_measure = SDDP.WorstCase(),
    iteration_limit = 10
)

However, if you want different risk measures at different nodes, there are two options. First, you can pass risk_measure a dictionary of risk measures, with one entry for each node. The keys of the dictionary are the indices of the nodes.

SDDP.train(
    model,
    risk_measure = Dict(
        1 => SDDP.Expectation(),
        2 => SDDP.WorstCase()
    ),
    iteration_limit = 10
)

An alternative method is to pass risk_measure a function that takes one argument, the index of a node, and returns an instance of a risk measure:

SDDP.train(
    model,
    risk_measure = (node_index) -> begin
        if node_index == 1
            return SDDP.Expectation()
        else
            return SDDP.WorstCase()
        end
    end,
    iteration_limit = 10
)

Note

If you simulate the policy, the simulated value is the risk-neutral value of the policy.

Supported risk measures

The following risk measures are implemented in SDDP.jl: