A simple implementation of backwards induction for solving finite-horizon, finite-state stochastic dynamic programs.
A simple implementation of backwards induction for solving finite-horizon, finite-space stochastic dynamic programs.
stochasticdp is available on PyPI:
pip install stochasticdp
To initialize a stochastic dynamic program:
dp = StochasticDP(number_of_stages, states, decisions, minimize)
number_of_stages is an integer
states is a list
decisions is a list
minimize is a boolean
This results in a stochastic dynamic program with stages numbered 0, ..., number_of_stages - 1, and initializes the following dictionaries:
dp.probability, where dp.probability[m, n, t, x] is the probability of moving from state n to state m in stage t under decision x
dp.contribution, where dp.contribution[m, n, t, x] is the immediate contribution of resulting from moving from state n to state m in stage t under decision x
dp.boundary, where dp.boundary[n] is the boundary condition for the value-to-go function at state n
You only need to define probabilities and contributions for transitions that occur with positive probability.
You can use the following helper functions to populate these dictionaries:
# This sets dp.probability[m, n, t, x] = p and dp.contribution[m, n, t, x] = c dp.add_transition(stage=t, from_state=n, decision=x, to_state=m, probability=p, contribution=c) # This sets dp.boundary[n] = v dp.boundary(state=n, value=v)
To solve the stochastic dynamic program:
value, policy = dp.solve()
value is a dictionary: value[t, n] is the value-to-go function at stage t and state n
policy is a dictionary: policy[t, n] is the set of optimizers of value[t, n]
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