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added support for different activations, number of hidden layers, options, running a big run over this

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Arec Jamgochian
2022-02-25 00:00:04 -08:00
parent 7feea74eb8
commit 99aa50a08b
3 changed files with 27 additions and 11 deletions
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18
sgail-ppo-options-setobs2.py
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@@ -7,6 +7,7 @@ from src.core.value import SetValue
from src.safe_options.policy import SetMaskedDiscretePolicy
from src.core.discriminator import DeepsetDiscriminator
import torch.optim
import torch.nn
from intersim.envs import IntersimpleLidarFlatRandom
from intersim.envs.intersimple import speed_reward
import functools
@@ -44,11 +45,14 @@ def training_function(config):
),
stop_on_collision=False,
), 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=config['policy']['option'], safe_actions_collision_method='circle', abort_unsafe_collision_method='circle') for _ in range(60)]
env_fn = lambda i: envs[i]
policy = SetMaskedDiscretePolicy(env_fn(0).action_space.n, hidden_layer_size=config['policy']['hidden_layer_size']) # config net architecture
policy = SetMaskedDiscretePolicy(env_fn(0).action_space.n,
n_hidden_layers=config['policy']['n_hidden_layers'],
hidden_layer_size=config['policy']['hidden_layer_size'],
activation=config['policy']['activation'] ) # config net architecture
pi_opt = torch.optim.Adam(policy.parameters(), lr=config['policy']['learning_rate'])
pi_lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(pi_opt, gamma=config['policy']['learning_rate_decay'])
@@ -105,7 +109,15 @@ analysis = tune.run(
'learning_rate_decay': tune.grid_search([1.0]),
'clip_ratio': tune.grid_search([0.2]),
'iterations_per_epoch': tune.grid_search([100]),
'hidden_layer_size': tune.grid_search([10])
'hidden_layer_size': tune.grid_search([10, 20, 30]),
'n_hidden_layers': tune.grid_search([1, 2, 3]),
'activation':tune.grid_search([torch.nn.LeakyReLU, torch.nn.Tanh]),
'option': tune.grid_search([[(0, 5), (1, 5), (2, 5), (4, 5), (6, 5), (8, 5), (10, 5)],
[(0, 5), (1, 5), (2, 5), (4, 5), (6, 5), (8, 5), (10, 5),
(0, 10), (1, 10), (2, 10), (4, 10), (6, 10), (8, 10), (10, 10)],
[(0, 5), (1, 5), (2, 5), (4, 5), (6, 5), (8, 5), (10, 5),
(0, 10), (1, 10), (2, 10), (4, 10), (6, 10), (8, 10), (10, 10),
(0, 20), (1, 20), (2, 20), (4, 20), (6, 20), (8, 20), (20, 10)]])
},
'value': {
'learning_rate': tune.grid_search([1e-3]),