FREEZE_INTERVAL = 1000
DETERMINISTIC = True
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]
FREEZE_INTERVAL = 1000
DETERMINISTIC = True
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_delta,
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.)
FREEZE_INTERVAL = 1000
DETERMINISTIC = True
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_delta,
parameters.freeze_interval, parameters.batch_size,
parameters.network_type, parameters.update_rule,
parameters.batch_accumulator, rng)
# --- 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)
FREEZE_INTERVAL = 1000
DETERMINISTIC = True
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_delta,
parameters.freeze_interval,
parameters.batch_size,
parameters.network_type,
parameters.update_rule,
parameters.batch_accumulator,
rng)