def play_it():
#ENV_NAME = 'CartPole-v0'
#ENV_NAME = 'MountainCar-v0'
ENV_NAME = 'Single_virtual-v0'
# Get the environment and extract the number of actions.
env = make(ENV_NAME)
env1 = make(ENV_NAME)
np.random.seed(123)
env.seed(123)
nb_actions = env.action_space.n
model = build_model(nb_actions,env.observation_space)
# model = build_model1(nb_actions, env.observation_space)
# Finally, we configure and compile our agent. You can use every built-in Keras optimizer and
# even the metrics!
memory = SequentialMemory(limit=50000, window_length=1)
policy = BoltzmannQPolicy()
dqn = DQNAgent(model=model, nb_actions=nb_actions, memory=memory, nb_steps_warmup=10,
target_model_update=1e-2, policy=policy,)
dqn.compile(Adam(lr=1e-3), metrics=['mae'])
# Okay, now it's time to learn something! We visualize the training here for show, but this
# slows down training quite a lot. You can always safely abort the training prematurely using
# Ctrl + C.
dqn.fit(env, nb_steps=50000, visualize=False, verbose=2)
# After training is done, we save the final weights.
dqn.save_weights(os.path.join('models_weights_logs','dqn_{}_weights.h5f'.format(ENV_NAME+ datetime.now().strftime("%Y%m%d-%H%M%S"))), overwrite=True)
# dqn.load_weights(os.path.join('models_weights_logs','dqn_{}_weights.h5f'.format(ENV_NAME)))
# Finally, evaluate our algorithm for 5 episodes.
dqn.test(env1, nb_episodes=5, visualize=True)
def main():
# parser = argparse.ArgumentParser(description='Run DQN on Atari SpaceInvaders')
# parser.add_argument('--env', default='SpaceInvaders-v0', help='Atari env name')
# parser.add_argument(
# '-o', '--output', default='SpaceInvaders-v0', help='Directory to save data to')
# parser.add_argument('--seed', default=0, type=int, help='Random seed')
# # parser.add_argument('--input_shape', default=(84, 84, 4), type=tuple, help='Size of each frame')
#
# args = parser.parse_args()
#
# args.output = get_output_folder(args.output, args.env)
#vehicle_network
veh_network = create_lstm_model(nb_time_steps,
nb_input_vector,
num_actions=g1)
#Attacker network
att_network = create_lstm_model(nb_time_steps,
nb_input_vector,
num_actions=gym.make(
args.env).action_space.n)
veh_agent = DQNAgent(q_network=veh_network,
preprocessor=core.Preprocessor(),
memory=core.ReplayMemory(),
policy=1,
gamma=0.1,
target_update_freq=100,
num_burn_in=100,
train_freq=20,
batch_size=32)
att_agent = DQNAgent(q_network=att_network,
preprocessor=core.Preprocessor(),
memory=core.ReplayMemory(),
policy=1,
gamma=0.1,
target_update_freq=100,
num_burn_in=100,
train_freq=20,
batch_size=32)
veh_agent.compile('Adam', 'mse')
att_agent.compile('Adam', 'mse')
env = VehicleFollowingENV
for i_episode in range(20):
agent.fit(env, 10**6)
# env.close()
model_json = q_network.to_json()
with open("model.json", "w") as json_file:
json_file.write(model_json)
def main():
# vehicle_network
veh_network = create_lstm_model(nb_time_steps, nb_input_vector, num_actions=4)
# Attacker network
# att_network = create_lstm_model(nb_time_steps, nb_input_vector, num_actions=4)
veh_agent = DQNAgent(q_network=veh_network,
q_network2=veh_network,
preprocessor=core.Preprocessor(),
RLmemory=core.ReplayMemory(),
SLmemory=core.ReplayMemory(),
policy=1,
gamma=0.1,
target_update_freq=100,
num_burn_in=100,
train_freq=20,
batch_size=32)
# att_agent = DQNAgent(q_network=att_network,
# q_network2=att_network,
# preprocessor=core.Preprocessor(),
# memory=core.ReplayMemory(),
# policy=1,
# gamma=0.1,
# target_update_freq=100,
# num_burn_in=100,
# train_freq=20,
# batch_size=32)
veh_agent.compile('Adam', 'mse')
# att_agent.compile('Adam', 'mse')
env = VehicleFollowingENV()
for i_episode in range(20):
veh_agent.fit(env=env, num_iterations=10 ** 6)
# att_agent.fit(env, 10 ** 6)
# env.close()
model_json = veh_network.to_json()
with open("model.json", "w") as json_file:
json_file.write(model_json)
value_max=1.0,
value_min=0.1,
value_test=0.05,
nb_steps=1000000)
dqn = DQNAgent(model=model,
nb_actions=3,
policy=policy,
memory=memory,
nb_steps_warmup=2000,
gamma=0.95,
target_model_update=2000,
train_interval=1,
delta_clip=1.0)
dqn.compile(optimizer, metrics=['mae'])
env = Tetris()
start = time.time()
weights_filename = 'dqn_{}_tetris_weights.h5'.format(start)
checkpoint_weights_filename = 'dqn_{}_tetris_weights_.h5'.format(start)
log_filename = 'dqn_tetris_log.json'
callbacks = [
ModelIntervalCheckpoint(checkpoint_weights_filename, interval=250000)
]
callbacks += [FileLogger(log_filename, interval=100)]
callbacks += [WandbLogger()]
dqn.fit(env, callbacks=callbacks, nb_steps=1750000, log_interval=10000)
dqn.save_weights(weights_filename, overwrite=True)
# If you want, you can experiment with the parameters or use a different policy. Another popular one
# is Boltzmann-style exploration:
# policy = BoltzmannQPolicy(tau=1.)
# Feel free to give it a try!
# creating both agents
hider_dqn = DQNAgent(model=hider_model,
nb_actions=nb_actions,
policy=policy,
memory=memory,
nb_steps_warmup=100,
gamma=.99,
target_model_update=10,
train_interval=2,
delta_clip=1.)
hider_dqn.compile(Adam(lr=.00025), metrics=['mae'])
seeker_dqn = DQNAgent(model=seeker_model,
nb_actions=nb_actions,
policy=policy,
memory=memory,
nb_steps_warmup=100,
gamma=.99,
target_model_update=10,
train_interval=2,
delta_clip=1.)
seeker_dqn.compile(Adam(lr=.00025), metrics=['mae'])
# passing both agents to framework
framework = MultiAgentFramework(dqagents=[hider_dqn, seeker_dqn])
if args.mode == 'train':
# the agent initially explores the environment (high eps) and then gradually sticks to what it knows
# (low eps). We also set a dedicated eps value that is used during testing. Note that we set it to 0.05
# so that the agent still performs some random actions. This ensures that the agent cannot get stuck.
policy = LinearAnnealedPolicy(EpsGreedyQPolicy(), attr='eps', value_max=1., value_min=.1, value_test=.05,
nb_steps=1000000)
# The trade-off between exploration and exploitation is difficult and an on-going research topic.
# If you want, you can experiment with the parameters or use a different policy. Another popular one
# is Boltzmann-style exploration:
# policy = BoltzmannQPolicy(tau=1.)
# Feel free to give it a try!
dqn = DQNAgent(model=model, nb_actions=nb_actions, policy=policy, window_length=WINDOW_LENGTH, memory=memory,
processor=processor, nb_steps_warmup=50000, gamma=.99, delta_range=(-1., 1.),
target_model_update=10000, train_interval=4)
dqn.compile(Adam(lr=.00025), metrics=['mae'])
if args.mode == 'train':
# Okay, now it's time to learn something! We capture the interrupt exception so that training
# can be prematurely aborted. Notice that you can the built-in Keras callbacks!
weights_filename = 'dqn_{}_weights.h5f'.format(args.env_name)
checkpoint_weights_filename = 'dqn_' + args.env_name + '_weights_{step}.h5f'
log_filename = 'dqn_{}_log.json'.format(args.env_name)
callbacks = [ModelIntervalCheckpoint(checkpoint_weights_filename, interval=250000)]
callbacks += [FileLogger(log_filename, interval=100)]
dqn.fit(env, callbacks=callbacks, nb_steps=1750000, log_interval=10000)
# After training is done, we save the final weights one more time.
dqn.save_weights(weights_filename, overwrite=True)
# Finally, evaluate our algorithm for 10 episodes.
# build model.
model = Sequential()
model.add(Flatten(input_shape=(1, ) + env.observation_space.shape))
model.add(Dense(432, activation='relu'))
model.add(Dense(216, activation='relu'))
model.add(Dense(144, activation='linear'))
model.add(Dense(nb_actions))
print(model.summary())
memory = SequentialMemory(limit=500000, window_length=1)
policy = BoltzmannQPolicy()
dqn = DQNAgent(model=model,
nb_actions=nb_actions,
memory=memory,
nb_steps_warmup=100,
target_model_update=1e-2,
policy=policy)
dqn.compile(Adam(lr=1e-2), metrics=['mae'])
#dqn.load_weights('E:\\git\\TensorFlow\\dqn_Snake_weights_2_weights.h5f')
dqn.fit(env,
nb_steps=500000,
visualize=visualize_training,
verbose=2,
callbacks=[tbCallBack])
# After training is done save the final weights.
dqn.save_weights('dqn_{}_weights.h5f'.format("Snake"), overwrite=True)
# evaluate our model for 5 episodes.
dqn.test(env, nb_episodes=500, visualize=True)
nb_actions = env.action_space.n
actions = np.arange(nb_actions)
policy = EpsGreedyQPolicy(1.0, 0.999)
memory = Memory(limit=50000, maxlen=1)
obs = env.reset()
agent = DQNAgent(actions=actions,
memory=memory,
update_interval=500,
train_interval=1,
batch_size=32,
memory_interval=1,
observation=obs,
input_shape=[len(obs)],
training=True,
policy=policy)
agent.compile()
result = []
for episode in range(500): # 1000エピソード回す
agent.reset()
observation = env.reset() # 環境の初期化
# observation, _, _, _ = env.step(env.action_space.sample())
observation = deepcopy(observation)
agent.observe(observation)
for t in range(250): # n回試行する
# env.render() # 表示
action = agent.act()
observation, reward, done, info = env.step(
action) # アクションを実行した結果の状態、報酬、ゲームをクリアしたかどうか、その他の情報を返す
observation = deepcopy(observation)
agent.observe(observation, reward, done)
model = Sequential()
model.add(Flatten(input_shape=(1,) + env.observation_space.shape))
model.add(Dense(16))
model.add(Activation('relu'))
model.add(Dense(16))
model.add(Activation('relu'))
model.add(Dense(16))
model.add(Activation('relu'))
model.add(Dense(nb_actions))
model.add(Activation('linear'))
print(model.summary())
# Finally, we configure and compile our agent. You can use every built-in Keras optimizer and
# even the metrics!
memory = SequentialMemory(limit=50000)
policy = BoltzmannQPolicy()
dqn = DQNAgent(model=model, nb_actions=nb_actions, memory=memory, nb_steps_warmup=10,
target_model_update=1e-2, policy=policy)
dqn.compile(Adam(lr=1e-3), metrics=['mae'])
# Okay, now it's time to learn something! We visualize the training here for show, but this
# slows down training quite a lot. You can always safely abort the training prematurely using
# Ctrl + C.
dqn.fit(env, nb_steps=50000, visualize=True, verbose=2)
# After training is done, we save the final weights.
dqn.save_weights('dqn_{}_weights.h5f'.format(ENV_NAME), overwrite=True)
# Finally, evaluate our algorithm for 5 episodes.
dqn.test(env, nb_episodes=5, visualize=True)