epsilon = 1
def policy_output_to_stochastic_action(output, action_space):
global epsilon
output = np.squeeze(output, axis=0)
epsilon -= 1.0 / DDPG_CFG.greedy_accel_noise_steps
greedy_noise = np.array([
max(epsilon, 0) * greedy_function(output[0], 0.0, 0.60, 0.30), # steer
max(epsilon, 0) * greedy_function(output[1], 0.5, 1.00, 0.10), # accel
max(epsilon, 0) * greedy_function(output[2], -0.1, 1.00, 0.05)
]) # brake
stochastic_action = greedy_noise + output
bounded = np.clip(stochastic_action, action_space.low, action_space.high)
return bounded
if __name__ == "__main__":
tf.logging.info(
"@@@ start ddpg training gym_torcs @@@ start time:{}".format(
time.ctime()))
# Generate a Torcs environment
env_train = torcs_env_wrapper(vision=False,
throttle=True,
gear_change=False,
port=3101)
train(env_train, agent_action, eval_mode=False)
# stochastic_action = output + noise_process.sample()
# bound to torcs scope
bounded = np.clip(stochastic_action, action_space.low, action_space.high)
return bounded
if __name__ == "__main__":
tf.logging.info(
"@@@ start ddpg training gym_torcs @@@ start time:{}".format(
time.ctime()))
# Generate a Torcs environment
env_train = torcs_env_wrapper(vision=False,
throttle=True,
gear_change=False,
port=3101)
# env_eval = torcs_env_wrapper(vision=True, throttle=True, gear_change=False,port=8888)
#TODO rewrite.
#steer, accel, brake .after greedy noise.
#valid noise value can make gradients happy.
mu = np.array([0, 0, 0])
# x0=np.array([0, 0.5, -0.1])
theta = np.array([0.15, 0.15, 0.15])
sigma = np.array([0.3, 0.3, 0.3])
# x0 = np.array([0.1, 0.3, 0.1])
#TODO start equal exploration on steer, brake, accel.
# x0 = np.array([-0.2, 0.0, 0.2])
x0 = np.array([-0.2, -0.2, 0.2])
noise_process = UO_Process(mu=mu, x0=x0, theta=theta, sigma=sigma, dt=1e-2)
train(env_train, env_train, agent_action, noise_process)
tf.nn.conv2d_transpose()
return bounded
if __name__ == "__main__":
tf.logging.info(
"@@@ start ddpg training gym_bipedal_walker_v2 @@@ start time:{}".
format(time.ctime()))
# Generate a Torcs environment
train_env = gym.make(id='BipedalWalker-v2')
eval_monitor = Monitor(gym.make(id='BipedalWalker-v2'),
directory=DDPG_CFG.eval_monitor_dir,
video_callable=lambda x: False,
resume=True)
mu = np.array([0.0, 0.0, 0.0, 0.0])
# x0=np.array([0, 0.5, -0.1])
theta = np.array([0.15, 0.15, 0.15, 0.15])
sigma = np.array([0.3, 0.3, 0.3, 0.3])
# x0 = np.array([0.1, 0.3, 0.1])
# TODO greedy accel in the begining
x0 = np.array([
-0.2,
0.2,
0.2,
0.2,
])
noise_process = UO_Process(mu=mu, x0=x0, theta=theta, sigma=sigma, dt=1e-2)
train(train_env, eval_monitor, agent_action, noise_process)
import time
import tensorflow as tf
from gym_torcs_train_low_dim import torcs_env_wrapper
from low_dim_train.train_agent_low_dim import train
DDPG_CFG = tf.app.flags.FLAGS # alias
DDPG_CFG.log_dir = 'eval/gym_torcs_low_dim/tf_log/'
DDPG_CFG.checkpoint_dir = 'eval/gym_torcs_low_dim/chk_pnt/'
DDPG_CFG.eval_monitor_dir = 'eval/gym_torcs_low_dim/eval_monitor/'
tf.logging.set_verbosity(tf.logging.INFO)
if __name__ == "__main__":
tf.logging.info("@@@ start ddpg evaluation gym_torcs @@@ start time:{}".format(time.ctime()))
# Generate a Torcs environment
env = torcs_env_wrapper(vision=True, throttle=True, gear_change=False)
train(env,None,eval_mode=True)