code: import pytest
  import torch
  
  from d3rlpy.models.encoders import DefaultEncoderFactory
  from d3rlpy.models.torch.dynamics import (
      ProbabilisticDynamicsModel,
      ProbabilisticEnsembleDynamicsModel,
      _compute_ensemble_variance,
  )
  
  from .model_test import DummyEncoder, check_parameter_updates
  
  
  @pytest.mark.parametrize("batch_size", [32])
  @pytest.mark.parametrize("observation_shape", [(100,)])
  @pytest.mark.parametrize("n_ensembles", [5])
  @pytest.mark.parametrize("variance_type", ["max", "data"])
  def test_compute_ensemble_variance(
      batch_size, observation_shape, n_ensembles, variance_type
  ):
      observations = torch.rand((batch_size, n_ensembles) + observation_shape)
      rewards = torch.rand(batch_size, n_ensembles, 1)
      variances = torch.rand(batch_size, n_ensembles, 1)
  
      if variance_type == "max":
          ref = variances.max(dim=1).values
      elif variance_type == "data":
          data = torch.cat([observations, rewards], dim=2)
          ref = (data.std(dim=1) ** 2).sum(dim=1, keepdims=True)
  
      variances = _compute_ensemble_variance(
          observations, rewards, variances, variance_type
      )
  
      assert variances.shape == (batch_size, 1)
      assert torch.allclose(variances, ref)
  
  
  @pytest.mark.parametrize("feature_size", [100])
  @pytest.mark.parametrize("action_size", [2])
  @pytest.mark.parametrize("batch_size", [32])
  def test_probabilistic_dynamics_model(feature_size, action_size, batch_size):
      encoder = DummyEncoder(feature_size, action_size, True)
      dynamics = ProbabilisticDynamicsModel(encoder)
  
      # check output shape
      x = torch.rand(batch_size, feature_size)
      action = torch.rand(batch_size, action_size)
      pred_x, pred_reward = dynamics(x, action)
      assert pred_x.shape == (batch_size, feature_size)
      assert pred_reward.shape == (batch_size, 1)
  
      # check variance
      _, _, variance = dynamics.predict_with_variance(x, action)
      assert variance.shape == (batch_size, 1)
  
      # TODO: check error
      reward = torch.rand(batch_size, 1)
      loss = dynamics.compute_error(x, action, reward, x)
      assert loss.shape == (batch_size, 1)
  
      # check layer connection
      check_parameter_updates(dynamics, (x, action, reward, x))
  
  
  @pytest.mark.parametrize("feature_size", [100])
  @pytest.mark.parametrize("action_size", [2])
  @pytest.mark.parametrize("batch_size", [32])
  @pytest.mark.parametrize("n_ensembles", [5])
  @pytest.mark.parametrize("variance_type", ["max", "data"])
  def test_probabilistic_ensemble_dynamics_dynamics_model(
      feature_size, action_size, batch_size, n_ensembles, variance_type
  ):
      encoder = DummyEncoder(feature_size, action_size, True)
      models = []
      for _ in range(n_ensembles):
          models.append(ProbabilisticDynamicsModel(encoder))
  
      dynamics = ProbabilisticEnsembleDynamicsModel(models)
  
      # check output shape
      x = torch.rand(batch_size, feature_size)
      action = torch.rand(batch_size, action_size)
      pred_x, pred_reward = dynamics(x, action)
      assert pred_x.shape == (batch_size, n_ensembles, feature_size)
      assert pred_reward.shape == (batch_size, n_ensembles, 1)
  
      # check variance without indices
      pred_x, pred_reward, variances = dynamics.predict_with_variance(
          x, action, variance_type=variance_type
      )
      assert pred_x.shape == (batch_size, n_ensembles, feature_size)
      assert pred_reward.shape == (batch_size, n_ensembles, 1)
      assert variances.shape == (batch_size, 1)
  
      # check variance with indices
      indices = torch.randint(n_ensembles, size=(batch_size,))
      pred_x, pred_reward, variances = dynamics.predict_with_variance(
          x, action, variance_type=variance_type, indices=indices
      )
      assert pred_x.shape == (batch_size, feature_size)
      assert pred_reward.shape == (batch_size, 1)
      assert variances.shape == (batch_size, 1)
  
      # TODO: check error
      reward = torch.rand(batch_size, 1)
      loss = dynamics.compute_error(x, action, reward, x)
  
      # check layer connection
      check_parameter_updates(dynamics, (x, action, reward, x))
   import argparse
  import numpy as np
  import os
  import torch
  
  import offline_agent
  import online_agent
  from utils.constants import env_list
  
  
  if __name__ == "__main__":
  	
  	parser = argparse.ArgumentParser()
  	parser.add_argument("--env", default="HalfCheetah-v2")        # OpenAI gym environment name
  	parser.add_argument("--seed", default=0, type=int)            # Sets Gym, PyTorch and Numpy seeds
  	parser.add_argument("--T", type=int, default=100)			  # epochs for offline algorithms and steps for online algorithms
  	parser.add_argument("--save_interval", default=1, type=float) # For online algos, this means the intervals of saving steps
  																  # and for offline algos, this means the intervals of saving epochs
  	parser.add_argument("--batch_size", default=128, type=int)    # Mini batch size for networks
  	parser.add_argument("--algo", type=str, default=None, 	 	  # select algos
  						choices=['BCloning', 'BCQ', 'offDDPG', 'BEAR', 'CQL', 'onlineDDPG', 'onlineSAC', 'MOPO', 'CRR']) 
  	parser.add_argument('--model_path', type=str, default='../models_try/')	# where to save model
  	parser.add_argument('--buffer_path', type=str, default='D4RLdata')		# where is the data for offline training
  	args = parser.parse_args()
  
  	print("---------------------------------------")
  	print(f'setting: training {args.algo}, Env: {args.env}, seed: {args.seed}')
  	print("---------------------------------------")
  
  	if not os.path.exists(args.model_path):
  		os.makedirs(args.model_path, exist_ok=True)
  
  	torch.manual_seed(args.seed)
  	np.random.seed(args.seed)
  	
  	state_dim = env_list[args.env]['state_dim'] #env.observation_space.shape[0]
  	action_dim = env_list[args.env]['action_dim'] #env.action_space.shape[0] 
  	max_action = env_list[args.env]['max_action'] #float(env.action_space.high[0])
  
  	device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
  	
  	online = args.algo in ['onlineDDPG', 'onlineSAC']
  
  	if online:
  		trainer = online_agent.OnlineAgent(state_dim, action_dim, batch_size=args.batch_size, algo=args.algo, device=device)
  	else:
  		trainer = offline_agent.OfflineAgent(state_dim, action_dim, batch_size=args.batch_size, algo=args.algo, device=device)
  	trainer.train(args, device=device)
  
  	
  	# os.makedirs(f'/mnt/exps/models/{args.model_path}/', exist_ok=True)
  	os.system(f'mv {args.model_path}/{args.algo} /mnt/exps/models_our/{args.model_path}/')
  	if args.algo == 'MOPO':
  		os.system(f'mv {args.model_path}/MOPO_SAC/ /mnt/exps/models_retry/{args.model_path}/')
  
apis: torch.rand torch.cat torch.randint torch.allclose
apis: torch.manual_seed torch.cuda.is_available numpy.random.seed