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commiting json files and intermediate settings used to run baseline methods around 02-26, 16ocklock. Next will set off big tuning runs for gail, gailtrpo, and bc (with committed values)

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Arec Jamgochian
2022-02-27 16:27:01 -08:00
parent 2cacdf691f
commit 5f77398fd0
9 changed files with 191 additions and 19 deletions
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48
bc-experiment.py
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@@ -44,18 +44,36 @@ def training_function(config):
np.random.seed(config['seed'])
torch.manual_seed(config['seed'])
envs = [Setobs(TransformObservation(CollisionPenaltyWrapper(
IntersimpleLidarFlatRandom(
n_rays=5,
reward=functools.partial(
speed_reward,
collision_penalty=0
),
check_collisions=True,
stop_on_collision=config['trainenv']['stop_on_collision'],
), collision_distance=6, collision_penalty=100),
lambda obs: (obs - obs_min) / (obs_max - obs_min + 1e-10)
)) for _ in range(60)]
# choose validation environment
if config['experiment'] == 'A':
envs = [Setobs(TransformObservation(CollisionPenaltyWrapper(
IntersimpleLidarFlatRandom(
n_rays=5,
reward=functools.partial(
speed_reward,
collision_penalty=0
),
check_collisions=True,
stop_on_collision=config['trainenv']['stop_on_collision'],
), collision_distance=6, collision_penalty=100),
lambda obs: (obs - obs_min) / (obs_max - obs_min + 1e-10)
)) for _ in range(60)]
elif config['experiment'] == 'B':
envs = sum([[Setobs(TransformObservation(CollisionPenaltyWrapper(
IntersimpleLidarFlatRandom(
n_rays=5,
reward=functools.partial(
speed_reward,
collision_penalty=0
),
check_collisions=True,
stop_on_collision=config['trainenv']['stop_on_collision'],
), collision_distance=6, collision_penalty=100),
lambda obs: (obs - obs_min) / (obs_max - obs_min + 1e-10)
)) for _ in range(15)] for track in range(4)],[])
else:
raise NotImplementedError
env_fn = lambda i: envs[i]
# load expert data
@@ -99,7 +117,7 @@ def training_function(config):
pi_opt.step()
pi_lr_scheduler.step()
if epoch % 100 == 0:
if epoch % 10 == 0:
gen_states, gen_actions, gen_rewards, gen_dones, gen_collisions = rollout(env_fn, policy.cpu(), n_episodes=60, max_steps_per_episode=200)
gen_mean_episode_length = (~gen_dones).sum() / gen_states.shape[0]
gen_mean_reward_per_episode = gen_rewards[~gen_dones].sum() / gen_states.shape[0]
@@ -114,7 +132,7 @@ def training_function(config):
# save model checkpoints
ep = epoch + 1
if (ep % 25 == 0):
if (ep % 50 == 0):
torch.save(policy.state_dict(), f'policy_epoch{ep}.pt')
# save model
@@ -124,7 +142,7 @@ if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--train', choices=['A', 'B'])
parser.add_argument('--epochs', type=int, default=200)
parser.add_argument('--epochs', type=int, default=1000)
parser.add_argument('--test', type=str, help='path to config file to run final training on')
parser.add_argument('--test_seeds', type=int, default=5)
parser.add_argument('--test_cpus', type=int, help='number of cpus available to split test seed training over')