Create transfer.py

transfer.py

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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()