def main(folderName):
# folderName = './log-files/SecondModel/Jan-30_20_38_36'
# chkp.print_tensors_in_checkpoint_file("{}/value_function.ckpt".format(folderName), tensor_name='', all_tensors=True)
env = myEnv()
obs_dim = len(env.observation_space())
act_dim = len(env.action_space())
obs_dim += 1 # add 1 to obs dimension for time step feature (see run_episode())
policy = Policy(obs_dim, act_dim, 0.003)
policy.restore(folderName)
trajectories = run_policy(env, policy, episodes=20)
def init_env(env_name): ''' Inicialize my environment and return dimension of observation and action spaces. Args: Env_name: str environment name (e.g. "SecondModel") Returns: 3-tuple Environment (object) Number of observation dimension (int) Number of action dimensions (int) ''' env = myEnv() obs_dim = len(env.observation_space()) act_dim = len(env.action_space()) return env, obs_dim, act_dim
def init_gym(env_name):
"""
Initialize gym environment, return dimension of observation
and action spaces.
Args:
env_name: str environment name (e.g. "Humanoid-v1")
Returns: 3-tuple
gym environment (object)
number of observation dimensions (int)
number of action dimensions (int)
"""
#env = gym.make(env_name)
env = myEnv()
obs_dim = len(env.observation_space())
act_dim = len(env.action_space())
return env, obs_dim, act_dim
def main(folderName, bestMode):
# folderName = './log-files/SecondModel/Jan-30_20_38_36'
# chkp.print_tensors_in_checkpoint_file("{}/value_function.ckpt".format(folderName), tensor_name='', all_tensors=True)
env = myEnv()
if not bestMode:
obs_dim = len(env.observation_space())
act_dim = len(env.action_space())
obs_dim += 1 # add 1 to obs dimension for time step feature (see run_episode())
scaler = Scaler(obs_dim)
policy = Policy(obs_dim, act_dim, 0.003)
policy.restore(folderName)
trajectories = run_policy(env, policy, scaler, episodes=20)
else:
sequences = recoverSequences(folderName)
for sequence in sequences:
runSeq(sequence['actions'], env)
