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import h5py
import numpy as np
class Buffer():
def __init__(self):
self._obs = None
self._actions = None
self._rewards = None
self._next_obs = None
self._terminals = None
class LatentReplayBuffer(object):
def __init__(self,
real_size: int,
latent_size: int,
obs_dim: int,
action_dim: int,
immutable: bool = False,
load_from: str = None,
silent: bool = False,
seed: int = 0):
self.immutable = immutable
self.buffers = dict()
self.sizes = {'real': real_size, 'latent': latent_size}
for key in ['real', 'latent']:
self.buffers[key] = Buffer()
self.buffers[key]._obs = np.full((self.sizes[key], obs_dim), float('nan'), dtype=np.float32)
self.buffers[key]._actions = np.full((self.sizes[key], action_dim), float('nan'), dtype=np.float32)
self.buffers[key]._rewards = np.full((self.sizes[key], 1), float('nan'), dtype=np.float32)
self.buffers[key]._next_obs = np.full((self.sizes[key], obs_dim), float('nan'), dtype=np.float32)
self.buffers[key]._terminals = np.full((self.sizes[key], 1), float('nan'), dtype=np.float32)
self._real_stored_steps = 0
self._real_write_location = 0
self._latent_stored_steps = 0
self._latent_write_location = 0
self._stored_steps = 0
self._random = np.random.RandomState(seed)
@property
def obs_dim(self):
return self._obs.shape[-1]
@property
def action_dim(self):
return self._actions.shape[-1]
def __len__(self):
return self._stored_steps
def save(self, location: str):
f = h5py.File(location, 'w')
f.create_dataset('obs', data=self.buffers['real']._obs[:self._real_stored_steps], compression='lzf')
f.create_dataset('actions', data=self.buffers['real']._actions[:self._real_stored_steps], compression='lzf')
f.create_dataset('rewards', data=self.buffers['real']._rewards[:self._real_stored_steps], compression='lzf')
f.create_dataset('next_obs', data=self.buffers['real']._next_obs[:self._real_stored_steps], compression='lzf')
f.create_dataset('terminals', data=self.buffers['real']._terminals[:self._real_stored_steps], compression='lzf')
f.close()
def load(self, location: str):
with h5py.File(location, "r") as f:
obs = np.array(f['obs'])
self._real_stored_steps = obs.shape[0]
self._real_write_location = obs.shape[0] % self.sizes['real']
self.buffers['real']._obs[:self._real_stored_steps] = np.array(f['obs'])
self.buffers['real']._actions[:self._real_stored_steps] = np.array(f['actions'])
self.buffers['real']._rewards[:self._real_stored_steps] = np.array(f['rewards'])
self.buffers['real']._next_obs[:self._real_stored_steps] = np.array(f['next_obs'])
self.buffers['real']._terminals[:self._real_stored_steps] = np.array(f['terminals'])
def add_samples(self, obs_feats, actions, next_obs_feats, rewards, terminals, sample_type='latent'):
if sample_type == 'real':
for obsi, actsi, nobsi, rewi, termi in zip(obs_feats, actions, next_obs_feats, rewards, terminals):
self.buffers['real']._obs[self._real_write_location] = obsi
self.buffers['real']._actions[self._real_write_location] = actsi
self.buffers['real']._next_obs[self._real_write_location] = nobsi
self.buffers['real']._rewards[self._real_write_location] = rewi
self.buffers['real']._terminals[self._real_write_location] = termi
self._real_write_location = (self._real_write_location + 1) % self.sizes['real']
self._real_stored_steps = min(self._real_stored_steps + 1, self.sizes['real'])
else:
for obsi, actsi, nobsi, rewi, termi in zip(obs_feats, actions, next_obs_feats, rewards, terminals):
self.buffers['latent']._obs[self._latent_write_location] = obsi
self.buffers['latent']._actions[self._latent_write_location] = actsi
self.buffers['latent']._next_obs[self._latent_write_location] = nobsi
self.buffers['latent']._rewards[self._latent_write_location] = rewi
self.buffers['latent']._terminals[self._latent_write_location] = termi
self._latent_write_location = (self._latent_write_location + 1) % self.sizes['latent']
self._latent_stored_steps = min(self._latent_stored_steps + 1, self.sizes['latent'])
self._stored_steps = self._real_stored_steps + self._latent_stored_steps
def sample(self, batch_size, return_dict: bool = False):
real_idxs = self._random.choice(self._real_stored_steps, batch_size)
latent_idxs = self._random.choice(self._latent_stored_steps, batch_size)
obs = np.concatenate([self.buffers['real']._obs[real_idxs],
self.buffers['latent']._obs[latent_idxs]], axis=0)
actions = np.concatenate([self.buffers['real']._actions[real_idxs],
self.buffers['latent']._actions[latent_idxs]], axis=0)
next_obs = np.concatenate([self.buffers['real']._next_obs[real_idxs],
self.buffers['latent']._next_obs[latent_idxs]], axis=0)
rewards = np.concatenate([self.buffers['real']._rewards[real_idxs],
self.buffers['latent']._rewards[latent_idxs]], axis=0)
terminals = np.concatenate([self.buffers['real']._terminals[real_idxs],
self.buffers['latent']._terminals[latent_idxs]], axis=0)
data = {
'obs': obs,
'actions': actions,
'next_obs': next_obs,
'rewards': rewards,
'terminals': terminals
}
return data
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