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cli.py
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cli.py
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"""Command-line interface for solving constrained POMDPs with ITLP."""
from typing import Sequence, Optional
import datetime
import pathlib
import json
import pickle
import numpy as np
import pandas as pd
import typer
import tqdm
import itlp_cpomdp.linear_programming
app = typer.Typer()
def get_g_prime(
prev_belief_state: np.ndarray,
action_index: int,
observation_index: int,
transition_probs: np.ndarray,
observation_probs: np.ndarray,
) -> np.ndarray:
"""Return the updated belief state."""
num_states = len(prev_belief_state)
g_prime = [None] * num_states
for state_index_j in range(num_states):
numerator = 0
for state_index_i in range(num_states):
numerator += (
prev_belief_state[state_index_i]
* transition_probs[action_index][state_index_i][state_index_j]
* observation_probs[action_index][state_index_j][observation_index]
)
g_prime[state_index_j] = numerator
denominator = sum(g_prime)
# assert denominator!=0
if denominator == 0:
g_prime[state_index_j] = prev_belief_state[state_index_j]
else:
g_prime[state_index_j] = numerator / denominator
g_prime = itlp_cpomdp.utils.round_sum_to_val(g_prime)
return g_prime
def calculate_f_values(
all_actions: np.ndarray,
transition_probs: np.ndarray,
all_observations: np.ndarray,
observation_probs: np.ndarray,
horizon: int,
all_grid_points: Sequence[Sequence[float]],
q_a_i: np.ndarray,
) -> np.ndarray:
"""Precompute the F values for setting up the LP."""
max_range_val = horizon + 1
# maximum reward over all actions a for each belief state k when t = 1
terminal_rewards = np.amax(np.einsum("ki,ai->ka", all_grid_points, q_a_i), axis=1)
prev_v_hat = terminal_rewards
F_values = np.zeros(
((max_range_val), len(all_actions), len(all_grid_points), len(all_grid_points))
)
g_prime_dict = {}
beta_calculator = itlp_cpomdp.linear_programming.StoredStateBetaCalculator(
all_grid_points, itlp_cpomdp.linear_programming.BetaObjective.STANDARD
)
for time in tqdm.trange(2, max_range_val, desc="Computing F Values"):
# calculate all beta for all possible action, observation, grid_value pair
all_beta_values = np.zeros(
(
len(all_actions),
len(all_observations),
len(all_grid_points),
len(all_grid_points),
)
)
for belief_state_index, belief_state in enumerate(all_grid_points):
for observation_index in range(len(all_observations)):
for action_index in range(len(all_actions)):
g_prime_key = (
action_index,
observation_index,
belief_state_index,
)
if g_prime_key in g_prime_dict:
g_prime = g_prime_dict[g_prime_key]
else:
g_prime = get_g_prime(
belief_state,
action_index,
observation_index,
transition_probs,
observation_probs,
)
g_prime_dict[g_prime_key] = g_prime
all_beta_values[
action_index, observation_index, belief_state_index, :
] = beta_calculator.get_backwards_induction_beta_values(
g_prime, prev_v_hat
)
# m, n are grid indices, i, j are state indices, o is observation index,
# a is action index
F_values[time] = np.einsum(
"mi,aij,ajo,aomn->amn",
all_grid_points,
transition_probs,
observation_probs,
all_beta_values,
optimize="optimal",
)
prev_v_hat = np.amax(
np.einsum("mi,ai->ma", all_grid_points, q_a_i, dtype=np.longdouble)
+ np.einsum("amn,n->ma", F_values[time], prev_v_hat, dtype=np.longdouble),
axis=1,
)
return F_values
def run_itlp(
horizon: int,
all_grid_points: np.ndarray,
budgets: Sequence[float],
all_costs: np.ndarray,
all_states: np.ndarray,
all_actions: np.ndarray,
all_observations: np.ndarray,
transition_probs: np.ndarray,
observation_probs: np.ndarray,
q_a_i: np.ndarray,
terminal_rewards: np.ndarray,
results_path: pathlib.Path,
delta: Optional[np.ndarray] = None,
):
"""Run the ITLP algorithm on a model."""
f_values_timer_start = datetime.datetime.now()
F_values = calculate_f_values(
all_actions=all_actions,
transition_probs=transition_probs,
all_observations=all_observations,
observation_probs=observation_probs,
horizon=horizon,
all_grid_points=all_grid_points,
q_a_i=q_a_i,
)
f_values_elapsed_time = (
datetime.datetime.now() - f_values_timer_start
).total_seconds()
delta = itlp_cpomdp.utils.round_sum_to_val(
[1 / len(all_grid_points)] * len(all_grid_points)
)
terminal_rewards = np.amax(np.einsum("ki,ai->ka", all_grid_points, q_a_i), axis=1)
policies = itlp_cpomdp.linear_programming.lp_grid_upper_bound_dual(
all_grid_points,
horizon,
len(all_actions),
len(all_states),
F_values,
q_a_i,
terminal_rewards,
np.array(budgets),
all_costs,
delta,
)
summary_results = []
for policy in policies:
fname = f"budget-{policy.budget}"
if policy.deterministic:
fname += "_deterministic"
fname += ".json"
fpath = results_path / fname
with open(fpath, "w", encoding="utf8") as fp:
policy.save(fp)
summary_results.append(
{
"budget": policy.budget,
"deterministic": policy.deterministic,
"V_LP": policy.objective_value,
"f_values_elapsed_time": f_values_elapsed_time,
"LP_time": policy.elapsed_time,
"optimize_time": policy.optimize_time,
}
)
df = pd.DataFrame(summary_results)
print(df)
df.to_excel(results_path / "summary_results.xlsx")
@app.command()
def train_constrained(
data_path: pathlib.Path,
results_path: pathlib.Path = pathlib.Path("results/tiger_model_test"),
horizon: int = 20,
budgets: list[float] = [21.0, 25.0, 50.0],
):
"""Train a constrained model using ITLP."""
with open(data_path / "model_parameters.json", "r", encoding="utf8") as fp:
model_parameters = json.load(fp)
with open(data_path / "precomputed_values.pickle", "rb") as fp:
precomputed_values = pickle.load(fp)
results_path.mkdir(exist_ok=True, parents=True)
run_itlp(
horizon=horizon,
budgets=budgets,
all_costs=np.array(model_parameters["all_costs"]),
all_states=np.array(model_parameters["all_states"]),
all_actions=np.array(model_parameters["all_actions"]),
all_observations=np.array(model_parameters["all_observations"]),
transition_probs=np.array(model_parameters["transition_probs"]),
observation_probs=np.array(model_parameters["observation_probs"]),
q_a_i=np.array(precomputed_values["q_a_i"]),
terminal_rewards=np.array(precomputed_values["terminal_rewards"]),
all_grid_points=np.array(precomputed_values["all_grid_points"]),
results_path=results_path,
)
if __name__ == "__main__":
app()