def init_agent_env(self, proc_id, role, role_id):
class Agent(A2C.Agent):
def build_model(self, name):
# randUni = tf.keras.initializers.RandomUniform(minval=-0.05, maxval=0.05, seed=None)
inputs = tf.keras.layers.Input(shape=self.state_size,
name='inputs')
# gru = tf.keras.layers.GRU(128, activation = 'tanh')(inputs)
common = tf.keras.layers.Dense(128, activation="relu")(inputs)
common = tf.keras.layers.Dense(128, activation="relu")(common)
action = tf.keras.layers.Dense(self.num_action,
activation="softmax",
name='action_outputs')(common)
critic = tf.keras.layers.Dense(1, name='value_output')(common)
model = tf.keras.Model(inputs=inputs, outputs=[action, critic])
return model
# env = remote.RemoteEnv()
env = cartPole.CartPoleEnv()
# env = flappyBird.FlappyBirdEnv()
NUM_STATE_FEATURES = env.get_num_state_features()
NUM_ACTIONS = env.get_num_actions()
PRINT_EVERY_EPISODE = 20
# LEARNING_RATE = 0.03
REWARD_DISCOUNT = 0.99
COEF_VALUE = 1
COEF_ENTROPY = 0
# agent = A2C.Agent((NUM_STATE_FEATURES, ), NUM_ACTIONS, REWARD_DISCOUNT, LEARNING_RATE, COEF_VALUE, COEF_ENTROPY)
agent = Agent((NUM_STATE_FEATURES, ), NUM_ACTIONS, REWARD_DISCOUNT,
LEARNING_RATE, COEF_VALUE, COEF_ENTROPY)
return agent, env
def init_agent_env(self, proc_id, role, role_id):
env = cartPole.CartPoleEnv()
# env = flappyBird.FlappyBirdEnv()
NUM_STATE_FEATURES = env.get_num_state_features()
NUM_ACTIONS = env.get_num_actions()
PRINT_EVERY_EPISODE = 20
LEARNING_RATE = 0.003
REWARD_DISCOUNT = 0.99
COEF_VALUE= 1
COEF_ENTROPY = 0
agent = A2C.Agent((NUM_STATE_FEATURES, ), NUM_ACTIONS, REWARD_DISCOUNT, LEARNING_RATE, COEF_VALUE, COEF_ENTROPY)
return agent, env
import numpy as np
# To run tqdm on notebook, import tqdm.notebook
# from tqdm.notebook import tqdm
# Run on pure python
from tqdm import tqdm
# Config Logging format
# logging.basicConfig(format='%(asctime)s - %(message)s', level=logging.INFO)
# Config logging module to enable on notebook
# logger = logging.getLogger()
# logger.setLevel(logging.DEBUG)
# Test GPU and show the available logical & physical GPUs
Util.test_gpu()
env = cartPole.CartPoleEnv()
NUM_STATE_FEATURES = env.get_num_state_features()
NUM_ACTIONS = env.get_num_actions()
EPISODE_NUM = 2000
PRINT_EVERY_EPISODE = 20
LEARNING_RATE = 0.003
REWARD_DISCOUNT = 0.99
exp_stg = EPSG.EpsilonGreedy(0.2, NUM_ACTIONS)
# agent = Agent((NUM_STATE_FEATURES, ), NUM_ACTIONS, REWARD_DISCOUNT, LEARNING_RATE, exp_stg)
agent_params = ((NUM_STATE_FEATURES, ), NUM_ACTIONS, REWARD_DISCOUNT,
LEARNING_RATE, exp_stg)
init_local_agent_funct = lambda: A2C.Agent(
(NUM_STATE_FEATURES,
import numpy as np
# To run tqdm on notebook, import tqdm.notebook
# from tqdm.notebook import tqdm
# Run on pure python
from tqdm import tqdm
# Config Logging format
# logging.basicConfig(format='%(asctime)s - %(message)s', level=logging.INFO)
# Config logging module to enable on notebook
# logger = logging.getLogger()
# logger.setLevel(logging.DEBUG)
# Test GPU and show the available logical & physical GPUs
Util.test_gpu()
env = cartPole.CartPoleEnv()
NUM_STATE_FEATURES = env.get_num_state_features()
NUM_ACTIONS = env.get_num_actions()
EPISODE_NUM = 200
PRINT_EVERY_EPISODE = 20
LEARNING_RATE = 0.03
REWARD_DISCOUNT = 0.99
COEF_VALUE = 1
COEF_ENTROPY = 0
exp_stg = EPSG.EpsilonGreedy(0.2, NUM_ACTIONS)
agent = A2C.Agent((NUM_STATE_FEATURES, ), NUM_ACTIONS, REWARD_DISCOUNT,
LEARNING_RATE, COEF_VALUE, COEF_ENTROPY)
# agent_params = ((NUM_STATE_FEATURES, ), NUM_ACTIONS, REWARD_DISCOUNT, LEARNING_RATE, exp_stg)
# init_local_agent_funct = lambda: Agent((NUM_STATE_FEATURES, ), NUM_ACTIONS, REWARD_DISCOUNT, LEARNING_RATE, exp_stg)