from blackbox_mpc.policies.mpc_policy import \
MPCPolicy
from blackbox_mpc.utils.dynamics_learning import learn_dynamics_from_policy
from blackbox_mpc.environment_utils import EnvironmentWrapper
from blackbox_mpc.utils.pendulum import pendulum_reward_function
import gym
import tensorflow as tf
env = gym.make("Pendulum-v0")
dynamics_function = DeterministicMLP(
layers=[
env.action_space.shape[0] + env.observation_space.shape[0], 32, 32, 32,
env.observation_space.shape[0]
],
activation_functions=[tf.math.tanh, tf.math.tanh, tf.math.tanh, None])
policy = RandomPolicy(number_of_agents=10, env_action_space=env.action_space)
dynamics_handler = learn_dynamics_from_policy(
env=EnvironmentWrapper.make_standard_gym_env("Pendulum-v0",
num_of_agents=10),
policy=policy,
number_of_rollouts=5,
task_horizon=200,
dynamics_function=dynamics_function)
mpc_policy = MPCPolicy(reward_function=pendulum_reward_function,
env_action_space=env.action_space,
env_observation_space=env.observation_space,
dynamics_handler=dynamics_handler,
optimizer_name='CEM')
current_obs = env.reset()
for t in range(200):
import tensorflow as tf
log_dir = './'
tf_writer = tf.summary.create_file_writer(log_dir)
env = HalfCheetahEnvModified()
num_of_agents = 1
parallel_env = EnvironmentWrapper.make_custom_gym_env(
HalfCheetahEnvModified, num_of_agents=num_of_agents)
dynamics_function = DeterministicMLP(
layers=[
env.action_space.shape[0] + env.observation_space.shape[0], 500, 500,
500, env.observation_space.shape[0]
],
activation_functions=[tf.math.tanh, tf.math.tanh, tf.math.tanh, None])
initial_policy = RandomPolicy(number_of_agents=num_of_agents,
env_action_space=env.action_space)
system_dynamics_handler, mpc_policy = learn_dynamics_iteratively_w_mpc(
env=parallel_env,
env_action_space=env.action_space,
env_observation_space=env.observation_space,
number_of_initial_rollouts=5,
number_of_rollouts_for_refinement=3,
number_of_refinement_steps=1,
task_horizon=1000,
planning_horizon=15,
initial_policy=initial_policy,
dynamics_function=dynamics_function,
num_agents=num_of_agents,
reward_function=reward_function,
log_dir=log_dir,