}
session.run(train_op, feed_dict)
session.run(target_update)
return session.run(q_loss, feed_dict)
# %% main loop
losses = []
returns = []
for n in range(ITERATIONS):
epsilon = 1.0 / np.sqrt(n + 1)
steps, total_return = play_once(env, epsilon)
returns.append(total_return)
if MINIMAL_SAMPLES < replay_buffer.number_of_samples():
loss = train()
losses.append(loss)
if n != 0 and n % 10 == 0:
print("Episode:", n, "Returns:", total_return, "epsilon:", epsilon)
#%% Demo
filename = os.path.basename(__file__).split('.')[0]
monitor_dir = './' + filename + '_' + str(datetime.now())
env = gym.wrappers.Monitor(env, monitor_dir)
for n in range(DEMO_NUMBER):
play_once(env, 0.0, render=True)
# %%Close Environment
def demo():
demo_env = gym.wrappers.Monitor(env,
MONITOR_DIR,
resume=True,
mode="evaluation",
write_upon_reset=True)
steps, total_return = play_once(demo_env, 0.05, render=True)
print("Demo for %d steps, Return %d" % (steps, total_return))
# Populate replay buffer
epsilon = 1.0
print("Populating replay buffer with epsilon %f..." % epsilon)
while MINIMAL_SAMPLES > replay_buffer.number_of_samples():
steps, total_return = play_once(env, epsilon, render=False)
print("Played %d < %d steps" %
(replay_buffer.number_of_samples(), MINIMAL_SAMPLES))
# Main loop
print("Start Main Loop...")
n = 0.5
for n in range(ITERATIONS):
epsilon = 1.0 / np.sqrt(n + 1)
steps, total_return = play_once(env, epsilon)
t0 = datetime.now()
for _ in range(steps):
train()
global_step += 1