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Save checkpoints and config to folder, move params to config

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ebuehrle
2022-02-26 12:23:42 +01:00
parent 5a5d8a7aff
commit f9d3cceed5
1 changed files with 27 additions and 9 deletions
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36
sgail-ppo-options-setobs2.py
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@@ -15,8 +15,11 @@ import numpy as np
from src.safe_options.options import SafeOptionsEnv from src.safe_options.options import SafeOptionsEnv
from torch.utils.tensorboard import SummaryWriter from torch.utils.tensorboard import SummaryWriter
from ray import tune from ray import tune
from datetime import datetime
import json
def training_function(config): def training_function(config):
DIR = os.path.dirname(os.path.abspath(__file__))
obs_min = np.array([ obs_min = np.array([
[-1000, -1000, 0, -np.pi, -1e-1, 0.], [-1000, -1000, 0, -np.pi, -1e-1, 0.],
[0, -np.pi, -20, -20, -np.pi, -1e-1], [0, -np.pi, -20, -20, -np.pi, -1e-1],
@@ -42,9 +45,11 @@ def training_function(config):
speed_reward, speed_reward,
collision_penalty=0 collision_penalty=0
), ),
stop_on_collision=False, stop_on_collision=config['env']['stop_on_collision'],
), collision_distance=6, collision_penalty=100), lambda obs: (obs - obs_min) / (obs_max - obs_min + 1e-10)) ), collision_distance=6, collision_penalty=100), lambda obs: (obs - obs_min) / (obs_max - obs_min + 1e-10))
), options=[(0, 5), (1, 5), (2, 5), (4, 5), (6, 5), (8, 5), (10, 5)], safe_actions_collision_method='circle', abort_unsafe_collision_method='circle') for _ in range(60)] ), options=[(0, 5), (1, 5), (2, 5), (4, 5), (6, 5), (8, 5), (10, 5)],
safe_actions_collision_method=config['env']['safe_actions_collision_method'],
abort_unsafe_collision_method=config['env']['abort_unsafe_collision_method']) for _ in range(60)]
env_fn = lambda i: envs[i] env_fn = lambda i: envs[i]
@@ -59,10 +64,10 @@ def training_function(config):
disc_opt = torch.optim.Adam(discriminator.parameters(), lr=config['discriminator']['learning_rate'], weight_decay=config['discriminator']['weight_decay']) disc_opt = torch.optim.Adam(discriminator.parameters(), lr=config['discriminator']['learning_rate'], weight_decay=config['discriminator']['weight_decay'])
expert_data = [ expert_data = [
torch.load(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'intersimple-expert-data-setobs2-loc0-track0.pt')), torch.load(os.path.join(DIR, 'intersimple-expert-data-setobs2-loc0-track0.pt')),
torch.load(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'intersimple-expert-data-setobs2-loc0-track1.pt')), torch.load(os.path.join(DIR, 'intersimple-expert-data-setobs2-loc0-track1.pt')),
torch.load(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'intersimple-expert-data-setobs2-loc0-track2.pt')), torch.load(os.path.join(DIR, 'intersimple-expert-data-setobs2-loc0-track2.pt')),
torch.load(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'intersimple-expert-data-setobs2-loc0-track3.pt')), torch.load(os.path.join(DIR, 'intersimple-expert-data-setobs2-loc0-track3.pt')),
] ]
d0 = [d[0] for d in expert_data] d0 = [d[0] for d in expert_data]
d1 = [d[1] for d in expert_data] d1 = [d[1] for d in expert_data]
@@ -71,8 +76,16 @@ def training_function(config):
expert_data = (torch.cat(d0), torch.cat(d1), torch.cat(d2), torch.cat(d3)) expert_data = (torch.cat(d0), torch.cat(d1), torch.cat(d2), torch.cat(d3))
expert_data = Buffer(*expert_data) expert_data = Buffer(*expert_data)
folder = str(datetime.now())
os.mkdir(os.path.join(DIR, folder))
with open(os.path.join(DIR, folder, 'config.json'), 'w') as f:
json.dump(config, f, indent=4)
def callback(info): def callback(info):
tune.report(gen_mean_reward_per_episode=info['gen/mean_reward_per_episode']) tune.report(gen_mean_reward_per_episode=info['gen/mean_reward_per_episode'])
if not info['epoch'] % 10:
torch.save(policy.state_dict(), os.path.join(DIR, folder, f'sgail-ppo-options-setobs2-{info["epoch"]}.pt'))
torch.save(value.state_dict(), os.path.join(DIR, folder, f'sgail-ppo-options-setobs2-value-{info["epoch"]}.pt'))
value, policy = gail_ppo( value, policy = gail_ppo(
env_fn=env_fn, env_fn=env_fn,
@@ -84,7 +97,7 @@ def training_function(config):
value=value, value=value,
v_opt=v_opt, v_opt=v_opt,
v_iters=config['value']['iterations_per_epoch'], v_iters=config['value']['iterations_per_epoch'],
epochs=200, epochs=301,
rollout_episodes=60, rollout_episodes=60,
rollout_steps=60, rollout_steps=60,
gamma=0.99, gamma=0.99,
@@ -100,12 +113,17 @@ def training_function(config):
analysis = tune.run( analysis = tune.run(
training_function, training_function,
config={ config={
'env': {
'stop_on_collision': False,
'safe_actions_collision_method': 'circle',
'abort_unsafe_collision_method': 'circle',
},
'policy': { 'policy': {
'learning_rate': tune.grid_search([3e-4]), 'learning_rate': tune.grid_search([3e-4]),
'learning_rate_decay': tune.grid_search([1.0]), 'learning_rate_decay': tune.grid_search([1.0]),
'clip_ratio': tune.grid_search([0.2]), 'clip_ratio': tune.grid_search([0.2]),
'iterations_per_epoch': tune.grid_search([100]), 'iterations_per_epoch': tune.grid_search([100]),
'hidden_layer_size': tune.grid_search([10]) 'hidden_layer_size': tune.grid_search([25])
}, },
'value': { 'value': {
'learning_rate': tune.grid_search([1e-3]), 'learning_rate': tune.grid_search([1e-3]),
@@ -114,7 +132,7 @@ analysis = tune.run(
'discriminator': { 'discriminator': {
'learning_rate': tune.grid_search([1e-3]), 'learning_rate': tune.grid_search([1e-3]),
'weight_decay': tune.grid_search([1e-4]), 'weight_decay': tune.grid_search([1e-4]),
'iterations_per_epoch': tune.grid_search([100]), 'iterations_per_epoch': tune.grid_search([500]),
} }
} }
) )