def path_discounted_returns(env,
gamma,
num_traj,
policy=test_policy(),
simpy=False,
printing=False):
# print('Env is of type ', type(env))
# print('Policy is of type ', type(policy))
if printing: print('Simulating %d Rollouts...' % (num_traj))
start_time = time.time()
if (not isinstance(env, TfEnv)):
env = TfEnv(env)
paths = []
rollout_times = []
if printing:
bar = progressbar.ProgressBar()
iterator = bar(range(num_traj))
else:
iterator = range(num_traj)
for i in iterator:
start_time_r = time.time()
if (simpy):
paths.append(ed_simpy_dec_rollout(env, policy))
else:
paths.append(ed_dec_rollout(env, policy))
elapsed_r = time.time() - start_time_r
rollout_times.append(elapsed_r)
paths = [item for sublist in paths for item in sublist]
adr = []
for path in paths:
t_sojourn = path["offset_t_sojourn"]
discount_gamma = np.exp(-gamma * t_sojourn)
path_adr = variable_discount_cumsum(path["rewards"], discount_gamma)
avg_discounted_return = path_adr[0]
adr.append(avg_discounted_return)
elapsed = time.time() - start_time
if printing:
print('Time Elapsed %.2f, or %.7f +- %.7f per rollout' %
(elapsed, mean(rollout_times),
std(rollout_times) / np.sqrt(num_traj)))
return mean(adr), std(adr) / np.sqrt(num_traj), adr
)
print(obj)
env = obj['env']
policy = obj['policy']
agents = policy
print('Learned Policy')
average_discounted_rewards = []
GAMMA = 0. #math.log(0.9)/(-5.) # decay to 90% in 5 seconds
for i in range(20):
paths = ed_dec_rollout(env, agents)
for path in paths:
t_sojourn = path["offset_t_sojourn"]
discount_gamma = np.exp(-GAMMA * t_sojourn)
path["returns"] = variable_discount_cumsum(
path["rewards"], discount_gamma)
average_discounted_rewards.append(sum(path["rewards"]))
if (i % 10 == 0):
print('Iteration: ', i)
print(len(average_discounted_rewards))
print(np.mean(average_discounted_rewards),
np.std(average_discounted_rewards))
from fire_smdp_params import test_policy_smarter