save_path=model_path)
if load_pretrained_network:
doomguy.load_model()
if train_network:
for episode in range(episodes):
print('Episode', episode)
doom.new_game()
done = False
step = 0
while not done:
state = doom.get_current_state()
action_index = doomguy.act(state)
next_state, reward, done = doom.step(action_index)
doomguy.remember(state, action_index, reward, next_state, done)
step += 1
loss = doomguy.train()
doomguy.reset_memory()
print('Total steps: {}, loss was: {}'.format(step, loss))
if show_results:
doom = VizdoomWrapper(config_path=config_path,
reward_table=reward_table,
frame_resolution=resolution,
show_mode=True,
frame_stack=4)
for episode in range(3):
doomguy.load_model()
for epoch in range(epochs):
print('\nEpoch {}\n-------'.format(epoch + 1))
print('\nTraining...')
doom.new_game()
train_scores = []
train_losses = []
prev_variables = []
tqdm_bar = tqdm.trange(training_episodes_per_epoch, leave=False)
for episode in tqdm_bar:
# Get state, action, reward, done and next state
state = doom.get_current_state()
best_action_index = doomguy.act(state)
next_state, reward, done = doom.step(best_action_index)
# Save to memory
doomguy.remember(state, best_action_index, reward, next_state, done)
# Replay from memory
training_loss = doomguy.replay(replay_batch_size)
train_losses.append(training_loss)
# Store results on game end
if done:
score = doom.get_total_reward()
train_scores.append(score)
doom.new_game()
if len(train_scores) > 0:
train_scores = np.array(train_scores)