scores = []
epsHistory = []
numGames = 500
batch_size = 50
#env = wrappers.Monitor(env, "tmp500/space-invaders-1", video_callable=lambda episode_id: True, force=True)
for i in range(numGames):
print('starting game ', i + 1, 'epsilon: %.4f' % brain.EPSILON)
epsHistory.append(brain.EPSILON)
done = False
observation = env.reset()
frames = [np.sum(observation[15:200, 30:125], axis=2)]
score = 0
lastAction = 0
while not done:
if len(frames) == 3:
action = brain.chooseAction(frames)
frames = []
else:
action = lastAction
observation_, reward, done, info = env.step(action)
score += reward
frames.append(np.sum(observation_[15:200, 30:125], axis=2))
if done and info['ale.lives'] == 0:
reward = -100
brain.storeTransition(
np.mean(observation[15:200, 30:125], axis=2), action, reward,
np.mean(observation_[15:200, 30:125], axis=2))
observation = observation_
brain.learn(batch_size)
lastAction = action
env.render()
msg = world_state.observations[-1].text
observations = json.loads(msg)
grid = observations.get(u'floor9x9', 0)
state = get_state(grid)
# Loop until mission ends
while world_state.is_mission_running:
print("-", end="")
time.sleep(0.1)
# action based on current state
# print(state)
# action_taken = np.random.choice(len(action_space))
# agent_host.sendCommand(action_space[action_taken])
action = brain.chooseAction(state)
agent_host.sendCommand(action_space[action])
world_state = agent_host.getWorldState()
for error in world_state.errors:
print("Error:", error.text)
# Have we received any observations
if world_state.number_of_observations_since_last_state > 0 and world_state.number_of_rewards_since_last_state > 0:
msg = world_state.observations[-1].text
observations = json.loads(msg)
grid = observations.get(u'floor9x9', 0)
# Get new_state & reward
def train():
print()
print("RUNNING THE MINECRAFT SIMULATION")
print()
RENDER = False
# RENDER = True
LOAD_MODEL = False
# LOAD_MODEL = True
start_eps = 0.8
WRAP = False
GRID_SIZE = 7
LOCAL_GRID_SIZE = 9 # Has to be an odd number (I think...)
SEED = 1
FOOD_COUNT = 1
OBSTACLE_COUNT = 0
# MAP_PATH = "./Maps/Grid{}/map2.txt".format(GRID_SIZE)
MAP_PATH = None
env = Environment(wrap=WRAP,
grid_size=GRID_SIZE,
rate=80,
max_time=30,
food_count=FOOD_COUNT,
obstacle_count=OBSTACLE_COUNT,
lava_count=0,
zombie_count=0,
action_space=5,
map_path=MAP_PATH)
brain = Agent(gamma=0.99,
epsilon=start_eps,
alpha=0.01,
maxMemorySize=10000,
replace=10)
if LOAD_MODEL:
try:
path = "./Models/Torch/my_model.pth"
brain.load_model(path)
print("Model loaded from path:", path)
print()
brain.EPSILON = 0.05
except Exception:
print('Could not load model')
print('Press <ENTER> to continue with random initialision')
print()
input()
# quit()
if RENDER: env.prerender()
games_played = 0
print("INITIALISING REPLAY MEMORY")
while brain.memCntr < brain.memSize:
observation, _ = env.reset()
# print(observation)
# observation = env.local_state_vector_3D()
done = False
if RENDER: env.render() # Render first screen
while not done:
action = brain.chooseAction(observation)
observation_, reward, done, info = env.step(action)
# observation_ = env.local_state_vector_3D()
# print(observation_)
if done:
# reward = -1
games_played += 1
brain.storeTransition(observation, action, reward, done,
observation_)
observation = observation_
if RENDER: env.render()
print("Done initialising replay memory. Played {} games".format(
games_played))
scores = []
epsHistory = []
numGames = 100000
print_episode = 100
batch_size = 16
avg_score = 0
avg_time = 0
avg_loss = 0
print()
print("TRAINING MODEL")
print()
for i in range(numGames):
epsHistory.append(brain.EPSILON)
done = False
observation, _ = env.reset()
# observation = env.local_state_vector_3D()
score = 0
lastAction = 0
if RENDER: env.render() # Render first screen
while not done:
action = brain.chooseAction(observation)
observation_, reward, done, info = env.step(action)
# observation_ = env.local_state_vector_3D()
# score += reward
# print(observation_)
brain.storeTransition(observation, action, reward, done,
observation_)
observation = observation_
loss = brain.learn(batch_size)
lastAction = action
if RENDER: env.render()
avg_score += info["score"]
avg_time += info["time"]
avg_loss += loss.item()
if i % print_episode == 0 and not i == 0 or i == numGames - 1:
print("Episode", i,
"\tavg time: {0:.3f}".format(avg_time / print_episode),
"\tavg score: {0:.3f}".format(avg_score / print_episode),
"\tavg loss: {0:.3f}".format(avg_loss / print_episode),
"\tepsilon: %.4f" % brain.EPSILON)
brain.save_model("./Models/Torch/my_model{}.pth".format(i))
avg_loss = 0
avg_score = 0
avg_time = 0
# scores.append(score)
# print("score:", score)
brain.save_model("./Models/Torch/my_model.pth")
# brain.load_model("./Models/Torch/my_model.pth")
# except Exception:
# print('Could not load model')
# quit()
for i in range(numGames):
epsHistory.append(brain.EPSILON)
done = False
obs, _ = env.reset()
observation = env.state_vector_3D()
score = 0
lastAction = 0
while not done:
action = brain.chooseAction(observation)
observation_, reward, done, info = env.step(action)
observation_ = env.state_vector_3D()
score += reward
brain.storeTransition(observation, action, reward, observation_)
observation = observation_
if TRAIN: loss = brain.learn(batch_size)
lastAction = action
if RENDER: env.render()
avg_score += info["score"]
if TRAIN: avg_loss += loss.item()