def rollout(env, t_begin, t_end, taken, gamma=0.97):
snap = snapshot(env, t_begin)
snap.populate(t_begin + 1, t_end, seed=clock_seed())
opt_take, opt_leave = compare_optimal(snap, t_begin + 1, t_end, set(taken))
m_take = get_n_matched(opt_take["matched"], t_begin, t_end)
m_leave = get_n_matched(opt_leave["matched"], t_begin, t_end)
m_take[0] = len(taken)
value_leave = disc_mean(m_leave, gamma)
value_take = disc_mean(m_take, gamma)
return value_take / value_leave
def rollout(env, t_begin, t_end, taken, gamma=0.97):
snap = snapshot(env, t_begin)
snap.populate(t_begin + 1, t_end, seed=clock_seed())
snap.removed_container[t_begin].update(taken)
value = greedy(snap, t_begin + 1, t_end)
matched = get_n_matched(value["matched"], t_begin, t_end)
matched[0] = len(taken)
return disc_mean(matched, gamma)
def rollout(env, t_begin, t_end, taken, gamma):
snap = snapshot(env, t_begin)
snap.populate(t_begin + 1, t_end, seed=clock_seed())
snap.removed_container[t_begin].update(taken)
opt = optimal(snap, t_begin + 1, t_end)
opt_matched = get_n_matched(opt["matched"], t_begin, t_end)
opt_matched[0] = len(taken)
opt_value = disc_mean(opt_matched, gamma)
# g = greedy(snap, t_begin+1, t_end)
# g_matched = get_n_matched(g["matched"], t_begin, t_end)
# g_matched[0] = len(taken)
# g_value = disc_mean(g_matched, gamma)
r = opt_value #- g_value
return r
disc = 0.1
net = torch.load("results/RNN_50-1-abo_4386504")
#%%
for k in [2]:
print("Creating environment")
env = ABOKidneyExchange(entry_rate, death_rate, time_length, seed=k)
print("Solving environment")
opt = optimal(env)
gre = greedy(env)
o = get_n_matched(opt["matched"], 0, env.time_length)
g = get_n_matched(gre["matched"], 0, env.time_length)
rewards = []
actions = []
t = -1
print("Beginning")
#%%
for t in range(env.time_length):
living = np.array(env.get_living(t))
if len(living) == 1:
continue
probs, counts = evaluate_policy(net, env, t)
_, cycles = get_cycles(env, living)
seed = 123456 #clock_seed()
print("Opening file", file)
try:
net = torch.load("results/" + file)
except Exception as e:
print(str(e))
continue
env_type = "abo"
env = ABOKidneyExchange(entry_rate, death_rate, max_time, seed=seed)
opt = optimal(env)
gre = greedy(env)
o = get_n_matched(opt["matched"], 0, env.time_length)
g = get_n_matched(gre["matched"], 0, env.time_length)
rewards = np.zeros(env.time_length)
#%%
np.random.seed(clock_seed())
for t in range(env.time_length):
probs, count = evaluate_policy(net, env, t, dtype="numpy")
for i in range(count):
probs, _ = evaluate_policy(net, env, t, dtype="numpy")
cycles = two_cycles(env, t)
if len(cycles) == 0:
break
elif len(cycles) == 1:
