action = action_to_one_hot_bool(agent.select_epsilon_greedy_action(state,epsilon))
for i in range(frame_repeat):
sleep(sleep_time)
reward = game.make_action(action, 1)
done = game.is_episode_finished()
state = next_state
reward_sum += reward
if done:
print('episode:', episode, 'sum_of_rewards_for_episode:', reward_sum)
total_reward.append(reward_sum)
break
return agent, total_reward
if __name__ == "__main__":
version = 'DDQN'
config_file = 'deadly_corridor'
checkpoint_file = './results/dqn/agent1/cp-0001.ckpt'
frame_repeat = 12
episodes = 5
config_path = 'D:/Joe/Anaconda3/envs/tensorflow_env/Lib/site-packages/vizdoom/scenarios/'+config_file+'.cfg'
game = initialize_vizdoom(config_path)
action_size = game.get_available_buttons_size()
print('action_size',action_size)
state_size = np.array(game.get_state().screen_buffer.shape)
if version=='DQN':
agent = DQN.DQN_Agent(state_size,action_size,checkpoint_file=checkpoint_file)
if version=='DDQN':
agent = Double_DQN.DQN_Agent(state_size,action_size,checkpoint_file=checkpoint_file)
_, total_reward = run_agent(agent, game, frame_repeat)
game.close()
#parameters
batch_size=6
learning_rate=0.001
folder=str(learning_rate)
replay_buffer_size=100000
m = 256
episodes = 50
n_agents = 1
for version in versions:
for config_file in config_files:
config_path = 'D:/Joe/Anaconda3/envs/tensorflow_env/Lib/site-packages/vizdoom/scenarios/'+config_file+'.cfg'
all_total_rewards = []
for n in range(n_agents):
game = initialize_vizdoom(config_path)
action_size = game.get_available_buttons_size()
print('action_size',action_size)
state_size = np.array(game.get_state().screen_buffer.shape)
if version=='DQN':
agent = DQN.DQN_Agent(state_size,action_size, batch_size=batch_size, learning_rate=learning_rate, replay_buffer_size=replay_buffer_size, checkpoint_file='./agent'+str(n)+'/cp-9999.ckpt')
if version=='DDQN':
agent = Double_DQN.DQN_Agent(state_size,action_size, batch_size=batch_size, learning_rate=learning_rate, replay_buffer_size=replay_buffer_size, checkpoint_file='./agent'+str(n)+'/cp-9999.ckpt')
_, total_reward = train_agent(agent, game, frame_repeat)
all_total_rewards.append(total_reward)
game.close()
all_total_rewards_to_save = np.array(all_total_rewards)
if version=='DQN':
np.save('./all_total_rewards_'+config_file+'_'+version+'.npy', all_total_rewards_to_save)
if version=='DDQN':
np.save('./all_total_rewards_'+config_file+'_'+version+'.npy', all_total_rewards_to_save)
