i += 1
else:
self.total_score += env.act(2)
from deer.agent import NeuralAgent
from deer.learning_algos.q_net_keras import MyQNetwork
from deer.learning_algos.AC_net_keras import MyACNetwork
from deer.policies import EpsilonGreedyPolicy
rng = np.random.RandomState(123456)
# TODO : best algorithm, hyperparameter tuning
if args.network == 'DQN':
network = MyQNetwork(environment=env,
batch_size=32,
double_Q=True,
random_state=rng)
elif args.network == 'DDPG':
network = MyACNetwork(environment=env, batch_size=32, random_state=rng)
agent = NeuralAgent(env,
network,
train_policy=EpsilonGreedyPolicy(network, env.nActions(),
rng, 0.0),
replay_memory_size=1000,
batch_size=32,
random_state=rng)
#agent.attach(bc.VerboseController())
if args.fname == 'baseline':
agent = EmpiricalTreatmentAgent(env)
if __name__ == "__main__":
logging.basicConfig(level=logging.INFO)
# --- Parse parameters ---
parameters = process_args(sys.argv[1:], Defaults)
if parameters.deterministic:
rng = np.random.RandomState(123456)
else:
rng = np.random.RandomState()
# --- Instantiate environment ---
env = Toy_env(rng)
# --- Instantiate qnetwork ---
qnetwork = MyQNetwork(env, parameters.rms_decay, parameters.rms_epsilon,
parameters.momentum, parameters.clip_norm,
parameters.freeze_interval, parameters.batch_size,
parameters.update_rule, rng)
train_policy = EpsilonGreedyPolicy(qnetwork, env.nActions(), rng, 0.1)
test_policy = EpsilonGreedyPolicy(qnetwork, env.nActions(), rng, 0.)
# --- Instantiate agent ---
agent = NeuralAgent(env,
qnetwork,
parameters.replay_memory_size,
max(env.inputDimensions()[i][0]
for i in range(len(env.inputDimensions()))),
parameters.batch_size,
rng,
train_policy=train_policy,
test_policy=test_policy)
from deer.learning_algos.q_net_keras import MyQNetwork
from deer.learning_algos.AC_net_keras import MyACNetwork
import deer.experiment.base_controllers as bc
from deer.policies import EpsilonGreedyPolicy
from misc.other_controllers import GaussianNoiseController
from misc.GaussianNoiseExplorationPolicy import GaussianNoiseExplorationPolicy
env = CellEnvironment(args.obs_type, args.resize, args.reward, args.network,
args.special)
rng = np.random.RandomState(777)
# TODO : best algorithm, hyperparameter tuning
if args.network == 'DQN':
network = MyQNetwork(environment=env,
batch_size=32,
freeze_interval=args.epochs[1],
double_Q=True,
random_state=rng)
agent = NeuralAgent(env,
network,
replay_memory_size=min(
int(args.epochs[0] * args.epochs[1] * 1.1),
100000),
batch_size=32,
random_state=rng)
agent.setDiscountFactor(0.95)
agent.attach(bc.FindBestController(validationID=0,
unique_fname=args.fname))
agent.attach(bc.VerboseController())
agent.attach(bc.TrainerController())
agent.attach(
""" import numpy as np from deer.agent import NeuralAgent from deer.learning_algos.q_net_keras import MyQNetwork from Toy_env import MyEnv as Toy_env import deer.experiment.base_controllers as bc rng = np.random.RandomState(123456) # --- Instantiate environment --- env = Toy_env(rng) # --- Instantiate qnetwork --- qnetwork = MyQNetwork(environment=env, random_state=rng) # --- Instantiate agent --- agent = NeuralAgent(env, qnetwork, random_state=rng) # --- Bind controllers to the agent --- # Before every training epoch, we want to print a summary of the agent's epsilon, discount and # learning rate as well as the training epoch number. agent.attach(bc.VerboseController()) # During training epochs, we want to train the agent after every action it takes. # Plus, we also want to display after each training episode (!= than after every training) the average bellman # residual and the average of the V values obtained during the last episode. agent.attach(bc.TrainerController()) # We also want to interleave a "test epoch" between each training epoch.