Replaced conditional_actnorm_wrapper

Pyrado/pyrado/sampling/parallel_evaluation.py

@@ -148,24 +148,3 @@ def eval_randomized_domain(pool: SamplerPool,
  # Run with progress bar
  with tqdm(leave=False, file=sys.stdout, unit='rollouts', desc='Sampling') as pb:
  return pool.run_map(_run_rollout_nom, init_states, pb)
-
-
-def conditional_actnorm_wrapper(env: Env, ex_dirs: list, idx: int):
- """
- Wrap the environment with an action normalization wrapper if the simulated environment had one.
-
- :param env: environment to sample from
- :param ex_dirs: list of experiment directories that will be loaded
- :param idx: index of the current directory
- :return: modified environment
- """
- # Get the simulation environment
- env_sim, _, _ = load_experiment(ex_dirs[idx])
-
- if typed_env(env_sim, ActNormWrapper) is not None:
- env = ActNormWrapper(env)
- print_cbt(f'Added an action normalization wrapper to {idx + 1}-th evaluation policy.', 'y')
- else:
- env = remove_env(env, ActNormWrapper)
- print_cbt(f'Removed an action normalization wrapper to {idx + 1}-th evaluation policy.', 'y')
- return env

Pyrado/pyrado/utils/experiments.py

@@ -55,6 +55,7 @@
 from pyrado.environment_wrappers.observation_partial import ObsPartialWrapper
 from pyrado.environment_wrappers.utils import typed_env
 from pyrado.environments.base import Env
+from pyrado.environments.real_base import RealEnv
 from pyrado.environments.sim_base import SimEnv
 from pyrado.logger.experiment import load_dict_from_yaml
 from pyrado.policies.adn import pd_linear, pd_cubic, pd_capacity_21_abs, pd_capacity_21, pd_capacity_32, \
@@ -213,7 +214,8 @@ def load_experiment(ex_dir: str, args: Any = None) -> (Union[SimEnv, EnvWrapper]
  return env, policy, kwout
 
 
-def wrap_like_other_env(env_targ: Env, env_src: [SimEnv, EnvWrapper], use_downsampling: bool = False) -> Env:
+def wrap_like_other_env(env_targ: Union[SimEnv, RealEnv], env_src: [SimEnv, EnvWrapper], use_downsampling: bool = False
+ ) -> Union[SimEnv, RealEnv]:
  """
  Wrap a given real environment like it's simulated counterpart (except the domain randomization of course).
 

Pyrado/scripts/evaluation/eval_policies_domain_grid_1d.py

@@ -42,12 +42,12 @@
 from pyrado.environment_wrappers.utils import typed_env
 from pyrado.environments.pysim.quanser_qube import QQubeSwingUpSim
 from pyrado.logger.experiment import setup_experiment, save_list_of_dicts_to_yaml
-from pyrado.sampling.parallel_evaluation import eval_domain_params, conditional_actnorm_wrapper
+from pyrado.sampling.parallel_evaluation import eval_domain_params
 from pyrado.sampling.sampler_pool import SamplerPool
 from pyrado.utils.argparser import get_argparser
 from pyrado.utils.checks import check_all_lengths_equal
 from pyrado.utils.data_types import dict_arraylike_to_float
-from pyrado.utils.experiments import load_experiment
+from pyrado.utils.experiments import load_experiment, wrap_like_other_env
 from pyrado.utils.input_output import print_cbt
 
 
@@ -174,10 +174,11 @@
 
  # Loading the policies
  ex_dirs = [osp.join(p, e) for p, e in zip(prefixes, ex_names)]
- policies = []
+ env_sim_list = []
+ policy_list = []
  for ex_dir in ex_dirs:
  _, policy, _ = load_experiment(ex_dir, args)
- policies.append(policy)
+ policy_list.append(policy)
 
  # Create one-dim results grid and ensure right number of rollouts
  param_list = param_grid(param_spec)
@@ -190,7 +191,7 @@
  df = pd.DataFrame(columns=['policy', 'ret', 'len', varied_param_key])
 
  # Evaluate all policies
- for i, policy in enumerate(policies):
+ for i, (env_sim, policy) in enumerate(zip(env_sim_list, policy_list)):
  # Create a new sampler pool for every policy to synchronize the random seeds i.e. init states
  pool = SamplerPool(args.num_workers)
 
@@ -201,8 +202,8 @@
  else:
  print_cbt('No seed was set', 'y')
 
- # Add an action normalization wrapper if the policy was trained with one
- env = conditional_actnorm_wrapper(env, ex_dirs, i)
+ # Add the same wrappers as during training
+ env = wrap_like_other_env(env, env_sim)
 
  # Sample rollouts
  ros = eval_domain_params(pool, env, policy, param_list, init_state)

Pyrado/scripts/evaluation/eval_policies_dr.py

@@ -39,12 +39,12 @@
 from pyrado.environments.pysim.quanser_ball_balancer import QBallBalancerSim
 from pyrado.environments.pysim.quanser_cartpole import QCartPoleSwingUpSim, QCartPoleStabSim
 from pyrado.logger.experiment import setup_experiment, save_list_of_dicts_to_yaml
-from pyrado.sampling.parallel_evaluation import eval_randomized_domain, conditional_actnorm_wrapper
+from pyrado.sampling.parallel_evaluation import eval_randomized_domain
 from pyrado.sampling.sampler_pool import SamplerPool
 from pyrado.utils.argparser import get_argparser
 from pyrado.utils.checks import check_all_lengths_equal
 from pyrado.utils.data_types import dict_arraylike_to_float
-from pyrado.utils.experiments import load_experiment
+from pyrado.utils.experiments import load_experiment, wrap_like_other_env
 from pyrado.utils.input_output import print_cbt
 
 
@@ -103,10 +103,11 @@
 
  # Loading the policies
  ex_dirs = [osp.join(p, e) for p, e in zip(prefixes, ex_names)]
- policies = []
+ env_sim_list = []
+ policy_list = []
  for ex_dir in ex_dirs:
- _, policy, _ = load_experiment(ex_dir, args)
- policies.append(policy)
+ env_sim, policy, _ = load_experiment(ex_dir, args)
+ policy_list.append(policy)
 
  # Fix initial state (set to None if it should not be fixed)
  init_state_list = [None]*args.num_ro_per_config
@@ -115,7 +116,7 @@
  df = pd.DataFrame(columns=['policy', 'ret', 'len'])
 
  # Evaluate all policies
- for i, policy in enumerate(policies):
+ for i, (env_sim, policy) in enumerate(zip(env_sim_list, policy_list)):
  # Create a new sampler pool for every policy to synchronize the random seeds i.e. init states
  pool = SamplerPool(args.num_workers)
 
@@ -126,8 +127,8 @@
  else:
  print_cbt('No seed was set', 'y')
 
- # Add an action normalization wrapper if the policy was trained with one
- env = conditional_actnorm_wrapper(env, ex_dirs, i)
+ # Add the same wrappers as during training
+ env = wrap_like_other_env(env, env_sim)
 
  # Sample rollouts
  ros = eval_randomized_domain(pool, env, pert, policy, init_state_list) # internally calls DomainRandWrapperLive

Pyrado/scripts/evaluation/eval_policies_nominal_domain.py

@@ -37,12 +37,12 @@
 from pyrado.environments.pysim.quanser_ball_balancer import QBallBalancerSim
 from pyrado.environments.pysim.quanser_cartpole import QCartPoleSwingUpSim, QCartPoleStabSim
 from pyrado.logger.experiment import setup_experiment, save_list_of_dicts_to_yaml
-from pyrado.sampling.parallel_evaluation import eval_nominal_domain, conditional_actnorm_wrapper
+from pyrado.sampling.parallel_evaluation import eval_nominal_domain
 from pyrado.sampling.sampler_pool import SamplerPool
 from pyrado.utils.argparser import get_argparser
 from pyrado.utils.checks import check_all_lengths_equal
 from pyrado.utils.data_types import dict_arraylike_to_float
-from pyrado.utils.experiments import load_experiment
+from pyrado.utils.experiments import load_experiment, wrap_like_other_env
 from pyrado.utils.input_output import print_cbt
 
 
@@ -96,10 +96,11 @@
 
  # Loading the policies
  ex_dirs = [osp.join(p, e) for p, e in zip(prefixes, ex_names)]
- policies = []
+ env_sim_list = []
+ policy_list = []
  for ex_dir in ex_dirs:
  _, policy, _ = load_experiment(ex_dir, args)
- policies.append(policy)
+ policy_list.append(policy)
 
  # Fix initial state (set to None if it should not be fixed)
  init_state_list = [None]*args.num_ro_per_config
@@ -108,7 +109,7 @@
  df = pd.DataFrame(columns=['policy', 'ret', 'len'])
 
  # Evaluate all policies
- for i, policy in enumerate(policies):
+ for i, (env_sim, policy) in enumerate(zip(env_sim_list, policy_list)):
  # Create a new sampler pool for every policy to synchronize the random seeds i.e. init states
  pool = SamplerPool(args.num_workers)
 
@@ -119,8 +120,8 @@
  else:
  print_cbt('No seed was set', 'y')
 
- # Add an action normalization wrapper if the policy was trained with one
- env = conditional_actnorm_wrapper(env, ex_dirs, i)
+ # Add the same wrappers as during training
+ env = wrap_like_other_env(env, env_sim)
 
  # Sample rollouts
  ros = eval_nominal_domain(pool, env, policy, init_state_list)