def main():
if(len(sys.argv) != 5):
print("usage:{} <env> <model_json> <weights> <directory>".format(sys.argv[0]))
return sys.exit()
env = gym.make(sys.argv[1])
env.frameskip = 1
with open(sys.argv[2]) as json_file:
model = model_from_json(json.load(json_file),{"Eq9":Eq9})
model.load_weights(sys.argv[3])
epsilon = 0.01
input_shape = (84,84)
history_size = 4
eval_size = 1
directory = sys.argv[4]
history_prep = HistoryPreprocessor(history_size)
atari_prep = AtariPreprocessor(input_shape,0,999)
numpy_prep = NumpyPreprocessor()
preprocessors = PreprocessorSequence([atari_prep, history_prep, numpy_prep]) #from left to right
policy = GreedyEpsilonPolicy(epsilon)
agent = DQNAgent(model, preprocessors, None, policy, 0.99, None,None,None,None)
env = gym.wrappers.Monitor(env,directory,force=True)
reward_arr, length_arr = agent.evaluate_detailed(env,eval_size,render=False, verbose=True)
def setUpClass(cls):
cls.env = gym.make("Breakout-v0")
history_prep = HistoryPreprocessor(4)
atari_prep = AtariPreprocessor((84, 84), 0, 999)
numpy_prep = NumpyPreprocessor()
cls.preprocessors = PreprocessorSequence(
[atari_prep, history_prep, numpy_prep]) #from left to right
cls.atari_prep = atari_prep
def __init__(self, q_network, target_netwrok, policy, gamma, num_burn_in,
train_freq, batch_size, config):
self.q = q_network
self.q_target = target_netwrok
self.memory = ReplayMemory(config)
self.policy = policy
self.gamma = gamma
self.num_burn_in = num_burn_in
self.train_freq = train_freq
self.batch_size = batch_size
self.currentIter = 0
self.currentEps = 0
self.currentReward = 0
self.config = config
#####
self.historyPre = HistoryPreprocessor(config)
self.AtariPre = AtariPreprocessor(config)
pass
def main(): # noqa: D103
parser = argparse.ArgumentParser(description='Run DQN on Atari Breakout')
parser.add_argument('--env', default='Breakout-v0', help='Atari env name')
parser.add_argument('-o',
'--output',
default='atari-v0',
help='Directory to save data to')
parser.add_argument('--seed', default=0, type=int, help='Random seed')
args = parser.parse_args()
#args.input_shape = tuple(args.input_shape)
#args.output = get_output_folder(args.output, args.env)
#set up environment model
env = gym.make(str(args.env))
NUM_ACTIONS = env.action_space.n #env.get_action_space().num_actions()
#make dqn agent
FRAMES_PER_STATE = 4
INPUT_SHAPE = (84, 84)
GAMMA = .99
NUM_ITERATIONS = 5000000
TARGET_UPDATE_FREQ = 10000
NUM_BURN_IN = 32
TRAIN_FREQ = 0
BATCH_SIZE = 32
REPLAY_MEM_SIZE = 1000000
REPLAY_START_SIZE = 50000
MAX_EPISODE_LEN = 10000
HELD_OUT_STATES_SIZE = 1000
IS_DOUBLE_Q = True
model = create_model(FRAMES_PER_STATE,
INPUT_SHAPE,
NUM_ACTIONS,
model_name='linear q_network')
plot_model(model, to_file='model.png')
target = create_model(FRAMES_PER_STATE,
INPUT_SHAPE,
NUM_ACTIONS,
model_name='linear q_network target')
preprocessor = HistoryPreprocessor(FRAMES_PER_STATE - 1)
memory = ReplayMemory(REPLAY_MEM_SIZE, FRAMES_PER_STATE)
held_out_states = ReplayMemory(HELD_OUT_STATES_SIZE, FRAMES_PER_STATE)
policy = LinearDecayGreedyEpsilonPolicy(1, .05, int(1e6))
agent = DQNAgent(model, target, preprocessor, memory, policy,
held_out_states, HELD_OUT_STATES_SIZE, GAMMA,
TARGET_UPDATE_FREQ, NUM_BURN_IN, TRAIN_FREQ, BATCH_SIZE,
REPLAY_START_SIZE, NUM_ACTIONS, IS_DOUBLE_Q)
# compile agent
adam = Adam(lr=0.0001)
agent.compile(adam, mean_huber_loss)
agent.fit(env, NUM_ITERATIONS, MAX_EPISODE_LEN)
def test_detail(self):
memory = ActionReplayMemory(250, 4) #test memory
memory_old = ActionReplayMemoryOld(250, 4)
index = 0
h_prep = HistoryPreprocessor(4)
np_prep = NumpyPreprocessor()
preprocessors = PreprocessorSequence([h_prep, np_prep])
for x in range(0, 1000):
axr = np.random.randint(0, 100, (84, 84))
prep_state = preprocessors.process_state_for_memory(axr)
memory.append(prep_state, 4, 5)
memory_old.append(prep_state, 4, 5)
for t in range(0, 10):
batch_size = 32
indexes = (np.random.randint(0,
memory._filled_size,
size=batch_size)).tolist()
curr_arr, next_arr, reward_arr, action_arr, terminal_arr = memory.sample(
batch_size, indexes)
curr_arr2, next_arr2, reward_arr2, action_arr2, terminal_arr2 = memory_old.sample(
batch_size, indexes)
for i, terminal in enumerate(terminal_arr):
empty_arr = np.zeros((84, 84))
for d in range(0, 4):
self.assertTrue(not np.all(curr_arr[i][:, :,
d] == empty_arr))
self.assertTrue(
np.all(curr_arr[i][:, :, d] == curr_arr2[i][:, :, d]))
if (indexes[i] >= 4):
self.assertTrue(
np.all(curr_arr[i][:, :,
1] == memory.survey(indexes[i] -
1)))
self.assertTrue(
np.all(curr_arr[i][:, :,
2] == memory.survey(indexes[i] -
2)))
self.assertTrue(
np.all(curr_arr[i][:, :,
3] == memory.survey(indexes[i] -
3)))
self.assertTrue(
np.all(curr_arr[i][:, :, 0] == curr_arr2[i][:, :, 0]))
def main():
if (len(sys.argv) != 6):
print("usage:{} <env> <model_json> <weights> <render> <random>".format(
sys.argv[0]))
return sys.exit()
env = gym.make(sys.argv[1])
env.frameskip = 1
with open(sys.argv[2]) as json_file:
model = model_from_json(json.load(json_file), {"Eq9": Eq9})
model.load_weights(sys.argv[3])
epsilon = 0.01
input_shape = (84, 84)
history_size = 4
eval_size = 100
render = (sys.argv[4] == "y")
history_prep = HistoryPreprocessor(history_size)
atari_prep = AtariPreprocessor(input_shape, 0, 999)
numpy_prep = NumpyPreprocessor()
preprocessors = PreprocessorSequence(
[atari_prep, history_prep, numpy_prep]) #from left to right
if (sys.argv[5] == "y"):
print("using random policy")
policy = UniformRandomPolicy(env.action_space.n)
else:
print("using greedy policy")
policy = GreedyEpsilonPolicy(epsilon)
agent = DQNAgent(model, preprocessors, None, policy, 0.99, None, None,
None, None)
agent.add_keras_custom_layers({"Eq9": Eq9})
reward_arr, length_arr = agent.evaluate_detailed(env,
eval_size,
render=render,
verbose=True)
print("\rPlayed {} games, reward:M={}, SD={} length:M={}, SD={}".format(
eval_size, np.mean(reward_arr), np.std(reward_arr),
np.mean(length_arr), np.std(reward_arr)))
print("max:{} min:{}".format(np.max(reward_arr), np.min(reward_arr)))
plt.hist(reward_arr)
plt.show()
def main(): # noqa: D103
parser = argparse.ArgumentParser(description='Run DQN on Atari Breakout')
parser.add_argument('--env', default='Breakout-v0', help='Atari env name')
parser.add_argument('-o',
'--output',
default='atari-v0',
help='Directory to save data to')
parser.add_argument('--seed', default=0, type=int, help='Random seed')
args = parser.parse_args()
#args.input_shape = tuple(args.input_shape)
#args.output = get_output_folder(args.output, args.env)
#set up environment model
env = gym.make(str(args.env))
NUM_ACTIONS = env.action_space.n #env.get_action_space().num_actions()
#make dqn agent
FRAMES_PER_STATE = 4
INPUT_SHAPE = (84, 84)
GAMMA = .99
NUM_ITERATIONS = 500000
TARGET_UPDATE_FREQ = 0
NUM_BURN_IN = 0
TRAIN_FREQ = 0
BATCH_SIZE = 0
model = create_model(FRAMES_PER_STATE,
INPUT_SHAPE,
NUM_ACTIONS,
model_name='linear q_network')
preprocessor = HistoryPreprocessor(FRAMES_PER_STATE - 1)
memory = None
policy = LinearDecayGreedyEpsilonPolicy(1, .05, 10e6)
agent = DQNAgent(model, preprocessor, memory, policy, GAMMA,
TARGET_UPDATE_FREQ, NUM_BURN_IN, TRAIN_FREQ, BATCH_SIZE)
#compile agent
adam = Adam(lr=0.0001)
loss = losses.mean_squared_error
agent.compile(adam, loss)
agent.fit(env, NUM_ITERATIONS)
def testHistoryPreprocessor(): a = np.array([[1,1],[1,1]]) b = np.array([[2,2],[2,2]]) c = np.array([[3,3],[3,3]]) d = np.array([[4,4],[4,4]]) e = np.array([[5,5],[5,5]]) hp = HistoryPreprocessor(a.shape, 3) history = np.array([[[0.,0.,0.], \ [0.,0.,0.]], \ [[0.,0.,0.], \ [0.,0.,0.]]]) assert(np.array_equal(hp.history, history)) history = np.array([[[0.,0.,1.], \ [0.,0.,1.]], \ [[0.,0.,1.], \ [0.,0.,1.]]]) assert(np.array_equal(hp.process_state_for_network(a), history)) history = np.array([[[0.,1.,2.], \ [0.,1.,2.]], \ [[0.,1.,2.], \ [0.,1.,2.]]]) assert(np.array_equal(hp.process_state_for_network(b), history)) history = np.array([[[1.,2.,3.], \ [1.,2.,3.]], \ [[1.,2.,3.], \ [1.,2.,3.]]]) assert(np.array_equal(hp.process_state_for_network(c), history)) history = np.array([[[2.,3.,4.], \ [2.,3.,4.]], \ [[2.,3.,4.], \ [2.,3.,4.]]]) assert(np.array_equal(hp.process_state_for_network(d), history)) history = np.array([[[3.,4.,5.], \ [3.,4.,5.]], \ [[3.,4.,5.], \ [3.,4.,5.]]]) assert(np.array_equal(hp.process_state_for_network(e), history))
def main():
parser = argparse.ArgumentParser(description='Run DQN on Atari Breakout')
parser.add_argument('--env', default='Breakout-v0', help='Atari env name')
parser.add_argument('-o',
'--output',
default='atari-v0',
help='Directory to save data to')
parser.add_argument('--seed', default=0, type=int, help='Random seed')
parser.add_argument('--type',
default="DQN",
help='Type of network to train. ()')
args = parser.parse_args()
#check if valid network type
network_types = [
"Linear", "LinearERTF", "DoubleLinear", "DQN", "DDQN", "Duling"
]
if (not (args.type in network_types)):
raise ValueError("Invalid network type.")
NETWORK_TYPE = args.type
#set up environment model
env = gym.make(str(args.env))
NUM_ACTIONS = env.action_space.n
#make dqn agent
"""
FRAMES_PER_STATE = 4
INPUT_SHAPE = (84,84)
GAMMA = .99
NUM_ITERATIONS = 1000000
TARGET_UPDATE_FREQ = 100000
BATCH_SIZE = 32
REPLAY_MEM_SIZE = 1000000
REPLAY_START_SIZE = 50000
MAX_EPISODE_LEN = 100
REWARD_SAMPLE = 1000
HELD_OUT_STATES_SIZE=1000
"""
FRAMES_PER_STATE = 4
INPUT_SHAPE = (84, 84)
GAMMA = .99
NUM_ITERATIONS = 20000
TARGET_UPDATE_FREQ = 1000
BATCH_SIZE = 32
REPLAY_MEM_SIZE = 1000000
REPLAY_START_SIZE = 1000
MAX_EPISODE_LEN = 10
REWARD_SAMPLE = 1000
HELD_OUT_STATES_SIZE = 1000
#retuns a list of models ie: [Online,None] or [Online,Target] or [OnlineA,OnlineB]
models = create_model(FRAMES_PER_STATE, INPUT_SHAPE, NUM_ACTIONS,
NETWORK_TYPE)
history = HistoryPreprocessor(FRAMES_PER_STATE - 1)
preprocessor = Preprocessor()
if (NETWORK_TYPE != "Linear"):
memory = ReplayMemory(REPLAY_MEM_SIZE, FRAMES_PER_STATE)
else:
memory = None
held_out_states = ReplayMemory(HELD_OUT_STATES_SIZE, FRAMES_PER_STATE)
policy = LinearDecayGreedyEpsilonPolicy(1, .05, int(1e6))
agent = DQNAgent(models[0], models[1], preprocessor, history, memory,
policy, GAMMA, TARGET_UPDATE_FREQ, BATCH_SIZE,
REPLAY_START_SIZE, NUM_ACTIONS, NETWORK_TYPE,
REWARD_SAMPLE, held_out_states, HELD_OUT_STATES_SIZE)
#compile agent
adam = Adam(lr=0.0001)
loss = mean_huber_loss
agent.compile(adam, loss)
agent.fit(env, NUM_ITERATIONS, MAX_EPISODE_LEN)
model_json = models[0].to_json()
with open(NETWORK_TYPE + "model.json", "w") as json_file:
json_file.write(model_json)
# serialize weights to HDF5
models[0].save_weights(NETWORK_TYPE + "model.h5")
print("Saved model to disk")
def main():
#env = gym.make("Enduro-v0")
#env = gym.make("SpaceInvaders-v0")
#env = gym.make("Breakout-v0")
model_name = "q2"
if (len(sys.argv) >= 2):
model_name = sys.argv[1]
if (len(sys.argv) >= 3):
env = gym.make(sys.argv[2])
else:
#env = gym.make("Enduro-v0")
env = gym.make("SpaceInvaders-v0")
#env = gym.make("Breakout-v0")
#no skip frames
env.frameskip = 1
input_shape = (84, 84)
batch_size = 1
num_actions = env.action_space.n
memory_size = 2 #2 because it need to save the current state and the future state, no matter what it gets, it will always just pick the earlier one
memory_burn_in_num = 1
start_epsilon = 1
end_epsilon = 0.01
decay_steps = 1000000
target_update_freq = 1 #no targeting
train_freq = 4 #How often you train the network
history_size = 4
history_prep = HistoryPreprocessor(history_size)
atari_prep = AtariPreprocessor(input_shape, 0, 999)
numpy_prep = NumpyPreprocessor()
preprocessors = PreprocessorSequence(
[atari_prep, history_prep, numpy_prep]) #from left to right
policy = LinearDecayGreedyEpsilonPolicy(start_epsilon, end_epsilon,
decay_steps)
linear_model = create_model(history_size, input_shape, num_actions,
model_name)
optimizer = Adam(lr=0.001,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-08,
decay=0.0)
loss_func = huber_loss
#linear_model.compile(optimizer, loss_func)
linear_model.summary()
random_policy = UniformRandomPolicy(num_actions)
#memory = ActionReplayMemory(1000000,4)
memory = ActionReplayMemory(memory_size, history_size)
#memory_burn_in(env,memory,preprocessors,memory_burn_in_num,random_policy)
#print(reward_arr)
#print(curr_state_arr)
agent = DQNAgent(linear_model, preprocessors, memory, policy, 0.99,
target_update_freq, None, train_freq, batch_size)
agent.compile(optimizer, loss_func)
agent.save_models()
agent.fit(env, 1000000, 100000)
class DQNAgent:
"""Class implementing DQN.
This is a basic outline of the functions/parameters you will need
in order to implement the DQNAgnet. This is just to get you
started. You may need to tweak the parameters, add new ones, etc.
Feel free to change the functions and funciton parameters that the
class provides.
We have provided docstrings to go along with our suggested API.
Parameters
----------
q_network: keras.models.Model
Your Q-network model.
preprocessor: deeprl_hw2.core.Preprocessor
The preprocessor class. See the associated classes for more
details.
memory: deeprl_hw2.core.Memory
Your replay memory.
gamma: float
Discount factor.
target_update_freq: float
Frequency to update the target network. You can either provide a
number representing a soft target update (see utils.py) or a
hard target update (see utils.py and Atari paper.)
num_burn_in: int
Before you begin updating the Q-network your replay memory has
to be filled up with some number of samples. This number says
how many.
train_freq: int
How often you actually update your Q-Network. Sometimes
stability is improved if you collect a couple samples for your
replay memory, for every Q-network update that you run.
batch_size: int
How many samples in each minibatch.
"""
def __init__(self, q_network, target_netwrok, policy, gamma, num_burn_in,
train_freq, batch_size, config):
self.q = q_network
self.q_target = target_netwrok
self.memory = ReplayMemory(config)
self.policy = policy
self.gamma = gamma
self.num_burn_in = num_burn_in
self.train_freq = train_freq
self.batch_size = batch_size
self.currentIter = 0
self.currentEps = 0
self.currentReward = 0
self.config = config
#####
self.historyPre = HistoryPreprocessor(config)
self.AtariPre = AtariPreprocessor(config)
pass
def compile(self, optimizer, loss_func):
"""Setup all of the TF graph variables/ops.
This is inspired by the compile method on the
keras.models.Model class.
This is a good place to create the target network, setup your
loss function and any placeholders you might need.
You should use the mean_huber_loss function as your
loss_function. You can also experiment with MSE and other
losses.
The optimizer can be whatever class you want. We used the
keras.optimizers.Optimizer class. Specifically the Adam
optimizer.
"""
pass
def calc_q_values(self, state, network):
"""Given a state (or batch of states) calculate the Q-values.
Basically run your network on these states.
Return
------
Q-values for the state(s)
"""
state_pre = np.zeros((1, 4, 84, 84), dtype=np.float32)
state_pre[0] = state
q_values = network.predict(state_pre, batch_size=1)[0]
return q_values
def select_action(self, state, network, **kwargs):
"""Select the action based on the current state.
You will probably want to vary your behavior here based on
which stage of training your in. For example, if you're still
collecting random samples you might want to use a
UniformRandomPolicy.
If you're testing, you might want to use a GreedyEpsilonPolicy
with a low epsilon.
If you're training, you might want to use the
LinearDecayGreedyEpsilonPolicy.
This would also be a good place to call
process_state_for_network in your preprocessor.
Returns
--------
selected action
"""
state_pre = np.zeros((1, 4, 84, 84), dtype=np.float32)
state_pre[0] = state
q_values = network.predict(state_pre, batch_size=1)[0]
return self.policy.select_action(q_values)
def fit(self, env, num_iterations, max_episode_length=None):
"""Fit your model to the provided environment.
Its a good idea to print out things like loss, average reward,
Q-values, etc to see if your agent is actually improving.
You should probably also periodically save your network
weights and any other useful info.
This is where you should sample actions from your network,
collect experience samples and add them to your replay memory,
and update your network parameters.
Parameters
----------
env: gym.Env
This is your Atari environment. You should wrap the
environment using the wrap_atari_env function in the
utils.py
num_iterations: int
How many samples/updates to perform.
max_episode_length: int
How long a single episode should last before the agent
resets. Can help exploration.
"""
cnt = np.long(0)
episode_rwd = 0
_screen_raw = self.process_env_reset(env) # Save to history
mse_loss, mae_metric = 0, 0
self.policy = UniformRandomPolicy(env.action_space.n)
evaluation_interval_cnt = 0
while cnt < num_iterations:
cnt += 1
evaluation_interval_cnt += 1
current_state = self.historyPre.get_current_state()
action = self.select_action(current_state, self.q) # Get action
_screen_next_raw, reward, isterminal, _ = env.step(
action) # take action, observe new
episode_rwd += reward
_screen_raw = self.process_one_screen(
_screen_raw, action, reward, _screen_next_raw, isterminal,
True) # Save to history, Memory
# print "\t state: %d, Step: %d, reward: %d, terminal: %d, Observe: %d" \
# % (np.matrix(_screen).sum(), action, reward, isterminal, np.matrix(_screen_next).sum())
# env.render()
if isterminal: # reset
if evaluation_interval_cnt >= self.config.evaluation_interval:
Aver_reward = self.evaluate(env,
self.config.eval_batch_num)
# print ("----------Evaluate, Average reward", Aver_reward)
evaluation_interval_cnt = 0
with open(self.config.rewardlog, "a") as log:
log.write(",".join([
str(int(cnt / self.config.evaluation_interval)),
str(Aver_reward)
]) + "\n")
_screen_raw = self.process_env_reset(env)
# print ("Episode End, iter: ", cnt, "last batch loss: ", mse_loss, 'last mae Metric: ', mae_metric, "Episode reward: ", episode_rwd)
episode_rwd = 0
if cnt >= self.num_burn_in and cnt % self.train_freq == 0: # update
samples = self.AtariPre.process_batch(
self.memory.sample(self.batch_size))
x = np.zeros(
(self.batch_size, self.config.history_length,
self.config.screen_height, self.config.screen_width),
dtype=np.float32)
y = np.zeros((self.batch_size, int(action_size(env))),
dtype=np.float32)
for _index in range(len(samples)):
sample = samples[_index]
x[_index] = np.copy(sample.state)
if sample.is_terminal:
y[_index] = self.calc_q_values(sample.state, self.q)
y[_index][sample.action] = sample.reward
else:
y[_index] = self.calc_q_values(sample.state, self.q)
q_next = max(
self.calc_q_values(
sample.next_state,
self.q_target)) # Use max to update
y[_index][sample.
action] = sample.reward + self.gamma * q_next
mse_loss, mae_metric = self.q.train_on_batch(x, y)
with open(self.config.losslog, "a") as log:
log.write(",".join(
[str(cnt /
4), str(mse_loss),
str(mae_metric)]) + "\n")
# print(cnt, mse_loss, mae_metric)
if cnt % self.config.target_q_update_step == 0: # Set q == q^
self.q_target.set_weights(self.q.get_weights())
if cnt == self.config.memory_size: # change Policy
self.policy = LinearDecayGreedyEpsilonPolicy(
1, 0.05, self.config.decayNum)
if cnt % (num_iterations / 3) == 0: # Save model
TimeStamp = datetime.datetime.strftime(datetime.datetime.now(),
"%y-%m-%d_%H-%M")
self.q.save_weights(
str(self.config.modelname) + '_' + TimeStamp +
'_weights.h5')
return mse_loss, mae_metric, self.q, self.q_target
def process_one_screen(self, screen_raw, action, reward, screen_next_raw,
isterminal, Is_train):
screen_32_next = self.AtariPre.process_state_for_network(
screen_next_raw)
screen_8 = self.AtariPre.process_state_for_memory(screen_raw)
self.historyPre.insert_screen(screen_32_next)
if Is_train:
self.memory.append(screen_8, action, reward, isterminal)
return screen_next_raw
def process_env_reset(self, env):
self.historyPre.reset()
screen_raw = env.reset()
screen_32 = self.AtariPre.process_state_for_network(screen_raw)
self.historyPre.insert_screen(screen_32)
return screen_raw
def evaluate(self, env, num_episodes):
"""Test your agent with a provided environment.
You shouldn't update your network parameters here. Also if you
have any layers that vary in behavior between train/test time
(such as dropout or batch norm), you should set them to test.
Basically run your policy on the environment and collect stats
like cumulative reward, average episode length, etc.
You can also call the render function here if you want to
visually inspect your policy.
"""
eval_policy = GreedyEpsilonPolicy(self.config.epsilon)
cumu_reward = 0
epscnt = 0
while epscnt < num_episodes:
isterminal = False
_screen_raw = self.process_env_reset(env) # Save to history
while not isterminal:
current_state = self.historyPre.get_current_state()
action = self.select_action_test(current_state,
eval_policy) # Get action
_screen_next_raw, reward, isterminal, _ = env.step(
action) # take action, observe new
cumu_reward += reward
_screen_raw = self.process_one_screen(
_screen_raw, action, reward, _screen_next_raw, isterminal,
True) # Save to history, Memory
epscnt += 1
return cumu_reward / num_episodes
def select_action_test(self, state, policy, **kwargs):
"""Select the action based on the current state.
You will probably want to vary your behavior here based on
which stage of training your in. For example, if you're still
collecting random samples you might want to use a
UniformRandomPolicy.
If you're testing, you might want to use a GreedyEpsilonPolicy
with a low epsilon.
If you're training, you might want to use the
LinearDecayGreedyEpsilonPolicy.
This would also be a good place to call
process_state_for_network in your preprocessor.
Returns
--------
selected action
"""
state_pre = np.zeros((1, 4, 84, 84), dtype=np.float32)
state_pre[0] = state
q_values = self.q.predict(state_pre, batch_size=1)[0]
return policy.select_action(q_values)
def main():
# load json and create model
json_file = open(
'/home/shivang/Desktop/HW2TomShivang/deeprl_hw2_src_DQNv2/model.json',
'r')
loaded_model_json = json_file.read()
json_file.close()
model = model_from_json(loaded_model_json)
# load weights into new model
model.load_weights("model.h5")
print("Loaded model from disk")
parser = argparse.ArgumentParser(description='Run DQN on Atari Breakout')
parser.add_argument('--env', default='Breakout-v0', help='Atari env name')
parser.add_argument('-o',
'--output',
default='atari-v0',
help='Directory to save data to')
parser.add_argument('--seed', default=0, type=int, help='Random seed')
args = parser.parse_args()
# args.input_shape = tuple(args.input_shape)
# args.output = get_output_folder(args.output, args.env)
# set up environment model
env1 = gym.make(str(args.env))
NUM_ACTIONS = env1.action_space.n # env.get_action_space().num_actions()
# make dqn agent
FRAMES_PER_STATE = 4
MAX_EPISODE_LEN = 1000
preprocessor = HistoryPreprocessor(FRAMES_PER_STATE - 1)
policy = LinearDecayGreedyEpsilonPolicy(1, .05, int(1e6))
preprocessor = HistoryPreprocessor(FRAMES_PER_STATE - 1)
# evaluate loaded model on test data
#compile agent
adam = Adam(lr=0.0001)
loss = mean_huber_loss
model.compile(loss=loss, optimizer=adam)
max_episode_length = MAX_EPISODE_LEN
num_episodes = 20
"""Test your agent with a provided environment.
You shouldn't update your network parameters here. Also if you
have any layers that vary in behavior between train/test time
(such as dropout or batch norm), you should set them to test.
Basically run your policy on the environment and collect stats
like cumulative reward, average episode length, etc.
You can also call the render function here if you want to
visually inspect your policy.
"""
cumulative_reward = 0
actions = np.zeros(env1.action_space.n)
no_op_max = 30
for episodes in range(num_episodes):
if episodes < 4:
env = wrappers.Monitor(
env1,
'/home/shivang/Desktop/HW2TomShivang/Video_evaluation/' +
str(episodes) + '/',
force=True)
else:
env = env1
# get initial state
preprocessor.reset()
preprocessor.process_state_for_network(env.reset())
state = preprocessor.frames
steps = 0
q_vals_eval = np.zeros(no_op_max)
for i in range(no_op_max):
q_vals = model.predict(state)
(next_state, reward, is_terminal, info) = env.step(0)
preprocessor.process_state_for_network(next_state)
next_state = preprocessor.frames
actions[0] += 1
steps = steps + 1
q_vals_eval[i] = q_vals_eval[i] + max(q_vals[0])
if is_terminal:
state = env.reset()
else:
state = next_state
while steps < max_episode_length:
q_vals = model.predict(state)
action = np.argmax(q_vals[0])
actions[action] += 1
(next_state, reward, is_terminal, info) = env.step(action)
# reward = self.preprocessor.process_reward(reward)
cumulative_reward = cumulative_reward + reward
preprocessor.process_state_for_network(next_state)
next_state = preprocessor.frames
state = next_state
steps = steps + 1
if is_terminal:
break
print(actions)
avg_reward = cumulative_reward / num_episodes
avg_qval = np.mean(q_vals_eval) / num_episodes
print(avg_reward)
print(avg_qval)
def main():
parser = argparse.ArgumentParser(description='Run DQN on Atari Breakout')
parser.add_argument('--env', default='Breakout-v0', help='Atari env name')
parser.add_argument('-o',
'--output',
default='atari-v0',
help='Directory to save data to')
parser.add_argument('--seed', default=0, type=int, help='Random seed')
parser.add_argument('--mode', choices=['train', 'test'], default='test')
parser.add_argument('--network',
choices=['deep', 'linear'],
default='deep')
parser.add_argument('--method',
choices=['dqn', 'double', 'dueling'],
default='dqn')
parser.add_argument('--monitor', type=bool, default=True)
parser.add_argument('--iter', type=int, default=2400000)
parser.add_argument('--test_policy',
choices=['Greedy', 'GreedyEpsilon'],
default='GreedyEpsilon')
args = parser.parse_args()
args.seed = np.random.randint(0, 1000000, 1)[0]
args.weights = 'models/dqn_{}_weights_{}_{}_{}.h5f'.format(
args.env, args.method, args.network, args.iter)
args.monitor_path = 'tmp/dqn_{}_weights_{}_{}_{}_{}'.format(
args.env, args.method, args.network, args.iter, args.test_policy)
if args.mode == 'train':
args.monitor = False
env = gym.make(args.env)
if args.monitor:
env = wrappers.Monitor(env, args.monitor_path)
np.random.seed(args.seed)
env.seed(args.seed)
args.gamma = 0.99
args.learning_rate = 0.0001
args.epsilon = 0.05
args.num_iterations = 5000000
args.batch_size = 32
args.window_length = 4
args.num_burn_in = 50000
args.target_update_freq = 10000
args.log_interval = 10000
args.model_checkpoint_interval = 10000
args.train_freq = 4
args.num_actions = env.action_space.n
args.input_shape = (84, 84)
args.memory_max_size = 1000000
args.output = get_output_folder(args.output, args.env)
args.suffix = args.method + '_' + args.network
if (args.method == 'dqn'):
args.enable_double_dqn = False
args.enable_dueling_network = False
elif (args.method == 'double'):
args.enable_double_dqn = True
args.enable_dueling_network = False
elif (args.method == 'dueling'):
args.enable_double_dqn = False
args.enable_dueling_network = True
else:
print('Attention! Method Worng!!!')
if args.test_policy == 'Greedy':
test_policy = GreedyPolicy()
elif args.test_policy == 'GreedyEpsilon':
test_policy = GreedyEpsilonPolicy(args.epsilon)
print(args)
K.tensorflow_backend.set_session(get_session())
model = create_model(args.window_length, args.input_shape,
args.num_actions, args.network)
# we create our preprocessor, the Ataripreprocessor will only process current frame the agent is seeing. And the sequence
# preprocessor will construct the state by concatenating 3 previous frames from HistoryPreprocessor and current processed frame
Processor = {}
Processor['Atari'] = AtariPreprocessor(args.input_shape)
Processor['History'] = HistoryPreprocessor(args.window_length)
ProcessorSequence = PreprocessorSequence(Processor) # construct 84x84x4
# we create our memory for saving all experience collected during training with window length 4
memory = ReplayMemory(max_size=args.memory_max_size,
input_shape=args.input_shape,
window_length=args.window_length)
# we use linear decay greedy epsilon policy and tune the epsilon from 1 to 0.1 during the first 100w iterations and then keep using
# epsilon with 0.1 to further train the network
policy = LinearDecayGreedyEpsilonPolicy(GreedyEpsilonPolicy(args.epsilon),
attr_name='eps',
start_value=1,
end_value=0.1,
num_steps=1000000)
# we construct our agent and use 0.99 as our discounted factor, 32 as our batch_size. We update our model for each 4 iterations. But during first
# 50000 iterations, we only collect data to the memory and don't update our model.
dqn = DQNAgent(q_network=model,
policy=policy,
memory=memory,
num_actions=args.num_actions,
test_policy=test_policy,
preprocessor=ProcessorSequence,
gamma=args.gamma,
target_update_freq=args.target_update_freq,
num_burn_in=args.num_burn_in,
train_freq=args.train_freq,
batch_size=args.batch_size,
enable_double_dqn=args.enable_double_dqn,
enable_dueling_network=args.enable_dueling_network)
adam = Adam(lr=args.learning_rate)
dqn.compile(optimizer=adam)
if args.mode == 'train':
weights_filename = 'dqn_{}_weights_{}.h5f'.format(
args.env, args.suffix)
checkpoint_weights_filename = 'dqn_' + args.env + '_weights_' + args.suffix + '_{step}.h5f'
log_filename = 'dqn_{}_log_{}.json'.format(args.env, args.suffix)
log_dir = '../tensorboard_{}_log_{}'.format(args.env, args.suffix)
callbacks = [
ModelIntervalCheckpoint(checkpoint_weights_filename,
interval=args.model_checkpoint_interval)
]
callbacks += [FileLogger(log_filename, interval=100)]
callbacks += [
TensorboardStepVisualization(log_dir=log_dir,
histogram_freq=1,
write_graph=True,
write_images=True)
]
# start training
# we don't apply action repetition explicitly since the game will randomly skip frame itself
dqn.fit(env,
callbacks=callbacks,
verbose=1,
num_iterations=args.num_iterations,
action_repetition=1,
log_interval=args.log_interval,
visualize=True)
dqn.save_weights(weights_filename, overwrite=True)
dqn.evaluate(env,
num_episodes=10,
visualize=True,
num_burn_in=5,
action_repetition=1)
elif args.mode == 'test':
weights_filename = 'dqn_{}_weights_{}.h5f'.format(
args.env, args.suffix)
if args.weights:
weights_filename = args.weights
dqn.load_weights(weights_filename)
dqn.evaluate(env,
num_episodes=250,
visualize=True,
num_burn_in=5,
action_repetition=1)
# we upload our result to openai gym
if args.monitor:
env.close()
gym.upload(args.monitor_path, api_key='sk_J62obX9PQg2ExrM6H9rvzQ')
def main():
#env = gym.make("Enduro-v0")
#env = gym.make("SpaceInvaders-v0")
#env = gym.make("Breakout-v0")
model_name = "result-q6-qqdn"
if (len(sys.argv) >= 2):
model_name = sys.argv[1]
if (len(sys.argv) >= 3):
env = gym.make(sys.argv[2])
else:
#env = gym.make("Enduro-v0")
env = gym.make("SpaceInvaders-v0")
#env = gym.make("Breakout-v0")
#no skip frames
env.frameskip = 1
input_shape = (84, 84)
batch_size = 32
num_actions = env.action_space.n
memory_size = 1000000
memory_burn_in_num = 50000
start_epsilon = 1
end_epsilon = 0.01
decay_steps = 1000000
target_update_freq = 10000
train_freq = 4 #How often you train the network
history_size = 4
history_prep = HistoryPreprocessor(history_size)
atari_prep = AtariPreprocessor(input_shape, 0, 999)
numpy_prep = NumpyPreprocessor()
preprocessors = PreprocessorSequence(
[atari_prep, history_prep, numpy_prep]) #from left to right
policy = LinearDecayGreedyEpsilonPolicy(start_epsilon, end_epsilon,
decay_steps)
model = create_model(history_size, input_shape, num_actions, model_name)
model.summary()
#plot_model(model,to_file="dueling.png")
optimizer = Adam(lr=0.001,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-08,
decay=0.0)
loss_func = huber_loss
#linear_model.compile(optimizer, loss_func)
random_policy = UniformRandomPolicy(num_actions)
#memory = ActionReplayMemory(1000000,4)
memory = ActionReplayMemory(memory_size, 4)
memory_burn_in(env, memory, preprocessors, memory_burn_in_num,
random_policy)
#print(reward_arr)
#print(curr_state_arr)
agent = DDQNAgent(model, preprocessors, memory, policy, 0.99,
target_update_freq, None, train_freq, batch_size)
agent.compile(optimizer, loss_func)
agent.save_models()
agent.fit(env, 1000000, 100000)
def main(): # noqa: D103
parser = argparse.ArgumentParser(description='Run DQN on Atari Breakout')
parser.add_argument('--env',
default='SpaceInvadersDeterministic-v3',
help='Atari env name')
parser.add_argument('-o',
'--output',
default='atari-v0',
help='Directory to save data to')
parser.add_argument('--seed', default=0, type=int, help='Random seed')
parser.add_argument('--model',
default='dqn',
help='Q Network type to use.')
parser.add_argument('--double', action='store_true')
model_map = {
'linear': LinearQN,
'mlp': MLP,
'dqn': DQN,
'dueling': DuelingDQN
}
args = parser.parse_args()
args.model = args.model.lower()
if args.model not in model_map:
print("Invalid model type. Valid types are", model_map.keys())
sys.exit(1)
args.output = get_output_folder(args.output, args.env)
# here is where you should start up a session,
# create your DQN agent, create your model, etc.
# then you can run your fit method.
env = gym.make(args.env)
monitored_env = gym.wrappers.Monitor(
gym.make(args.env),
args.output,
video_callable=lambda i: i % EVAL_NUM_EPISODES == 0)
atari = not args.env.startswith("CartPole")
if atari:
input_shape = (IMAGE_SIZE, IMAGE_SIZE)
preprocessor = lambda: PreprocessorSequence(
AtariPreprocessor(new_size=input_shape),
HistoryPreprocessor(history_length=WINDOW_SIZE, max_over=True))
else:
input_shape = (4, )
preprocessor = lambda: HistoryPreprocessor(history_length=WINDOW_SIZE)
memory = ExperienceReplay(max_size=REPLAY_BUFFER_SIZE,
window_length=WINDOW_SIZE)
NUM_ACTIONS = env.action_space.n
#policy = UniformRandomPolicy(num_actions=NUM_ACTIONS)
#policy = GreedyEpsilonPolicy(NUM_ACTIONS, EPSILON)
policy = LinearDecayGreedyEpsilonPolicy(NUM_ACTIONS, 1.0, EPSILON,
NUM_ITERATIONS_LINEAR_DECAY)
model = model_map[args.model](exp_name=args.output)
agent = DQNAgent(q_network=model,
preprocessor=preprocessor,
memory=memory,
policy=policy,
gamma=GAMMA,
target_update_freq=TARGET_UPDATE_FREQ,
replay_buffer_size=REPLAY_BUFFER_SIZE,
train_freq=TRAIN_FREQ,
batch_size=BATCH_SIZE,
output_dir=args.output,
double_dqn=args.double)
agent.compile(window=WINDOW_SIZE,
input_shape=input_shape,
num_actions=NUM_ACTIONS,
model_name='q_network')
signal.signal(signal.SIGINT, agent.signal_handler)
signal.signal(signal.SIGTERM, agent.signal_handler)
signal.signal(signal.SIGHUP, agent.signal_handler)
agent.fit(env, monitored_env, num_iterations=NUM_ITERATIONS)
def testPerformance(self):
"""
Test to make sure each model(DQN, DDQN, DoubleQN) could be created and compiled
"""
#create a model of the world
env = gym.make("SpaceInvaders-v0")
env.frameskip = 1
#create a fake keras model
input_shape = (84, 84)
window = 4
num_actions = env.action_space.n
model = Sequential(name="test_model")
model.add(
Convolution2D(filters=16,
kernel_size=8,
strides=4,
activation='relu',
input_shape=(input_shape[0], input_shape[1],
window)))
model.add(
Convolution2D(filters=32,
kernel_size=4,
strides=2,
activation='relu'))
model.add(
Convolution2D(filters=64,
kernel_size=3,
strides=1,
activation='relu'))
model.add(Flatten())
model.add(Dense(units=512, activation='relu'))
model.add(Dense(units=num_actions, activation='linear'))
#create loss function & optimizer
optimizer = Adam(lr=0.001,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-08,
decay=0.0)
loss_func = huber_loss
#preprocessors
history_prep = HistoryPreprocessor(4)
atari_prep = AtariPreprocessor(input_shape, 0, 999)
numpy_prep = NumpyPreprocessor()
preprocessors = PreprocessorSequence(
[atari_prep, history_prep, numpy_prep]) #from left to right
memory = ActionReplayMemory(100000, 4)
#policy = LinearDecayGreedyEpsilonPolicy(1, 0.1,100000)
policy = SamePolicy(1)
#agent = DQNAgent(model, preprocessors, memory, policy,0.99, target_update_freq,None,train_freq,batch_size)
dqn_agent = DQNAgent(model, preprocessors, memory, policy, 0.99, 10000,
None, 4, 32)
dqn_agent.compile(optimizer, loss_func)
total_time = 0
times = 50
for i in range(0, times):
start_time = time.time()
dqn_agent.evaluate_detailed(env, 1)
total_time += (time.time() - start_time)
sys.stdout.write('\r{}'.format(i))
sys.stdout.flush()
print("average evaluation time:{} total time:{}".format(
total_time / times, total_time))
