def __init__()

in utils/data_sampler.py [0:0]

• 19 lines of code
• 1 McCabe index (conditional complexity)

	def __init__(self, data, device, reward_tune='no'):
		
		self.state = torch.from_numpy(data['observations']).float()
		self.action = torch.from_numpy(data['actions']).float()
		self.next_state = torch.from_numpy(data['next_observations']).float()
		reward = torch.from_numpy(data['rewards']).view(-1, 1).float()
		self.not_done = 1. - torch.from_numpy(data['terminals']).view(-1, 1).float()

		self.size = self.state.shape[0]
		self.state_dim = self.state.shape[1]
		self.action_dim = self.action.shape[1]

		self.device = device

		if reward_tune == 'normalize':
			reward = (reward - reward.mean()) / reward.std()
		elif reward_tune == 'iql_antmaze':
			reward = reward - 1.0
		elif reward_tune == 'iql_locomotion':
			reward = iql_normalize(reward, self.not_done)
		elif reward_tune == 'cql_antmaze':
			reward = (reward - 0.5) * 4.0
		self.reward = reward