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import d4rl.gym_mujoco
import gym
import gymnasium
import minari
import numpy as np
def get_tuple_from_minari_dataset(dataset_name):
dt = minari.load_dataset(dataset_name)
observations, actions, rewards, next_observations, terminations, truncations = \
[], [], [], [], [], []
traj_length = []
for _ep in dt:
observations.append(_ep.observations[:-1])
actions.append(_ep.actions)
rewards.append(_ep.rewards)
next_observations.append(_ep.observations[1:])
terminations.append(_ep.terminations)
truncations.append(_ep.truncations)
traj_length.append(len(_ep.rewards))
assert (_ep.truncations[-1] or _ep.terminations[-1])
observations, actions, rewards, next_observations, terminations, truncations = \
map(np.concatenate, [observations, actions, rewards, next_observations, terminations, truncations])
traj_length = np.array(traj_length)
return observations, actions, rewards, next_observations, terminations, truncations, traj_length
def step_tuple_to_traj_tuple(obs, act, rew, next_obs, term, trunc):
dones = np.logical_or(term, trunc)[:-1] # last one should not be used for split to avoid empty chunk
dones_ind = np.where(dones)[0] + 1
obs, act, rew, next_obs, term, trunc = \
map(lambda x: np.split(x, dones_ind), [obs, act, rew, next_obs, term, trunc])
obs_new = [np.concatenate([_obs, _next_obs[-1].reshape(1, -1)])
for _obs, _next_obs in zip(obs, next_obs)]
buffer = []
keys = ['observations', 'actions', 'rewards', 'terminations', 'truncations']
for _traj_dt in zip(obs_new, act, rew, term, trunc):
_buff_i = dict(zip(keys, _traj_dt))
buffer.append(_buff_i)
return buffer
def make_traj_based_buffer(d4rl_env_name):
env = gym.make(d4rl_env_name)
dt = env.get_dataset()
obs = dt['observations']
next_obs = dt['next_observations']
rewards = dt['rewards']
actions = dt['actions']
terminations = dt['terminals']
truncations = dt['timeouts']
buffer = step_tuple_to_traj_tuple(obs, actions, rewards, next_obs, terminations, truncations)
return buffer, env
def create_standard_d4rl():
mujoco_envs = ['Hopper', 'HalfCheetah', 'Ant', 'Walker2d']
quality_lists = ['expert', 'medium', 'random', 'medium-expert']
for _env_prefix in mujoco_envs:
for _quality in quality_lists:
env_name = f'{_env_prefix.lower()}-{_quality}-v2'
buffer, env = make_traj_based_buffer(env_name)
if not (buffer[-1]["terminations"][-1] or buffer[-1]["truncations"][-1]):
buffer[-1]["truncations"][-1] = True
gymnasium_env = gymnasium.make(f'{_env_prefix}-v2')
dataset = minari.create_dataset_from_buffers(
dataset_id=env_name,
env=gymnasium_env,
buffer=buffer,
algorithm_name='SAC',
author='Zhiyuan',
# minari_version=f"{minari.__version__}",
author_email='levi.huzhiyuan@gmail.com',
code_permalink='TODO',
ref_min_score=env.ref_min_score,
ref_max_score=env.ref_max_score,
)
print('dataset created')
return
def validate_standard_d4rl():
mujoco_envs = ['Hopper', 'HalfCheetah', 'Ant', 'Walker2d']
quality_lists = ['expert', 'medium', 'random', 'medium-expert']
for _env_prefix in mujoco_envs:
for _quality in quality_lists:
env_name = f'{_env_prefix.lower()}-{_quality}-v2'
minari_tuple = get_tuple_from_minari_dataset(env_name)
m_obs, m_act, m_rew, m_next_obs, m_term, m_trunc, m_traj_len = minari_tuple
d4rl_data = gym.make(f'{_env_prefix.lower()}-{_quality}-v2').get_dataset()
assert np.all(m_act == d4rl_data["actions"])
assert np.all(m_obs == d4rl_data["observations"])
assert np.all(m_next_obs == d4rl_data["next_observations"])
assert np.all(m_rew == d4rl_data["rewards"])
assert np.all(m_term == d4rl_data["terminals"])
assert np.all(m_trunc[:-1] == d4rl_data["timeouts"][:-1])
assert m_trunc[-1]
d4rl_dones = np.logical_or(d4rl_data["terminals"], d4rl_data["timeouts"])[:-1]
# last one will always be added
d4rl_dones = np.where(d4rl_dones)[0]
num_d4rl = len(d4rl_data["rewards"])
d4rl_dones = np.concatenate([[-1], d4rl_dones, [num_d4rl - 1]])
d4rl_traj_length = d4rl_dones[1:] - d4rl_dones[:-1]
assert np.all(d4rl_traj_length == m_traj_len)
assert np.sum(m_traj_len) == len(m_rew)
print('validation passed')
return
create_standard_d4rl()
validate_standard_d4rl() |