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committing changes to start testing framework, removing shuffling of data

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This commit is contained in:
Arec
2022-01-17 15:47:40 -08:00
parent d34fa5774d
commit 2c1dc6ca33
3 changed files with 54 additions and 28 deletions
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2
src/data/__init__.py
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@@ -1 +1 @@
from src.data.expert import single_agent_demonstrations, multi_agent_demonstrations, NoShuffleRNG, load_experts, process_experts
from src.data.expert import single_agent_expert, single_agent_demonstrations, multi_agent_demonstrations, NoShuffleRNG, load_experts, process_experts

39
src/data/expert.py
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@@ -112,7 +112,7 @@ class NoShuffleRNG(np.random.RandomState):
def shuffle(self, x):
return x
def load_experts(expert_files, flatten = True):
def load_experts(expert_files, flatten=True):
"""
Load expert trajectories from files and combine their transitions into a single RB
@@ -131,8 +131,27 @@ def load_experts(expert_files, flatten = True):
transitions = rollout.flatten_trajectories(transitions)
return transitions
def single_agent_demonstrations(expert='NormalizedIntersimpleExpert',
env='NRasterizedRouteIncrementingAgent',
def single_agent_expert(expert='NormalizedIntersimpleExpert',
env='NRasterizedRouteIncrementingAgent',
env_args={}, policy_args={}, **kwargs):
"""
Args:
expert (class): class of expert
env (class): class of env intersim.envs.intersimple
env_args (dict): dictionary of kwargs when instantiating environment class
policy_args (dict): dictionary of kwargs when instantiating Expert policy
path (str): path to store output
min_timesteps (int): min number of timesteps for call to rollout.rollout_and_save
min_episodes (int): min number of episodes for call to rollout.rollout_and_save
video (bool): whether to save a video of the expert until a single environment instantiation stops
"""
Env = intersim.envs.intersimple.__dict__[env]
Expert = globals()[expert]
env = Env(**env_args)
policy = Expert(env, **policy_args)
single_agent_demonstrations(env, policy, **kwargs)
def single_agent_demonstrations(env, policy,
path=None, min_timesteps=None,
min_episodes=None, video=False,
env_args={}, policy_args={}):
@@ -141,8 +160,8 @@ def single_agent_demonstrations(expert='NormalizedIntersimpleExpert',
Usage:
python -m intersimple.expert <flags>
Args:
expert (class): class of expert
env (class): class of env intersim.envs.intersimple
env (class): intersimple environment
policy (BasePolicy): intersimple policy
path (str): path to store output
min_timesteps (int): min number of timesteps for call to rollout.rollout_and_save
min_episodes (int): min number of episodes for call to rollout.rollout_and_save
@@ -151,14 +170,8 @@ def single_agent_demonstrations(expert='NormalizedIntersimpleExpert',
policy_args (dict): dictionary of kwargs when instantiating Expert policy
"""
Env = intersim.envs.intersimple.__dict__[env]
Expert = globals()[expert]
env = Env(**env_args)
info_env = RolloutInfoWrapper(env) # getting rollout info (dictionary) from environment
venv = DummyVecEnv([lambda: info_env]) # making a DummyVecEnv with a list of a function that when called returns the rollout info
policy = Expert(env, **policy_args) # instantiate an expert policy from specified class with instantiated environment and policy kwargs
venv_policy = DummyVecEnvPolicy([lambda: policy]) # make a DummyVecEnvPolicy with a list of a function that when called returns the Expert policy
if min_timesteps is None and min_episodes is None:
@@ -166,7 +179,7 @@ def single_agent_demonstrations(expert='NormalizedIntersimpleExpert',
if video:
save_video(env, policy)
path = path or (policy.__class__.__name__ + '_' + env.__class__.__name__ + '.pkl')
suntil = rollout.make_sample_until(
min_timesteps=min_timesteps,
@@ -253,7 +266,7 @@ def process_experts(filename:str='expert.pkl',
policy_args=expert_args
)
# Single-Agent POV Demonstrations
single_agent_demonstrations(
single_agent_expert(
expert=expert_class,
env=env_class,
path=it_path,