Using a Trained Policy#

Training produces a policy: the collection of cuts. Two methods put it to work: policy() answers a single “what should I do here?” question, and simulate() evaluates the policy across many random paths.

Point queries with `policy()`#

policy() solves one stage at a given situation: a stage, the incoming state, and the realised noise.

decision = sddp.policy(stage="mar", state=150, noise=350, report=[R, L, Z, F])

decision.decisions          # {'R': 200.0, 'L': 250.0, 'Z': 0.0, 'F': 50.0}
decision.approx_cost_to_go  # 625.0

The returned PolicyResult carries the queried stage, the incoming_state and noise, the optimal decisions for the reported variables, and approx_cost_to_go, the immediate stage cost plus the cut-approximated future cost from here on.

state is a scalar for a single state variable, or a dict keyed by state-variable name when there are several.
report lists the variables whose optimal level to return; it defaults to the state variables. A time-only variable reports a float; a variable with extra dimensions reports a dict keyed by the non-time labels.

Note

policy() needs a trained policy. Calling it before train() warns that no cuts exist yet and returns a decision that is not the trained policy.

Monte Carlo with `simulate()`#

simulate() runs the policy forward on fresh sampled paths and returns the realised cost distribution.

sim = sddp.simulate(n_paths=1000, seed=0)
print(sim.summary)

n_paths    1000.000000
mean        140.000000
std         438.283119
p5            0.000000
p50           0.000000
p95        1500.000000
max        2500.000000
Name: total_cost, dtype: float64

The SimulationResult holds the per-path total_cost together with the per-(path, stage) stage_costs, noise and reported variables; its summary reduces total_cost to the mean, standard deviation and percentiles above. report defaults to the state variables, and seed defaults to the training seed plus one.

Both policy() and simulate() work on an instance reloaded from a .sddp file, so a saved policy can be queried without retraining.

Using a Trained Policy#

Point queries with policy()#

Monte Carlo with simulate()#

Point queries with `policy()`#

Monte Carlo with `simulate()`#