getting best shail/hail configs outsave up and running, adding shell for test runs
This commit is contained in:
Arec Jamgochian
33
best_configs/hail_expA.json
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33
best_configs/hail_expA.json
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{
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"experiment": "A",
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"trainenv": {
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"stop_on_collision": false,
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"safe_actions_collision_method": null,
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"abort_unsafe_collision_method": null
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},
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"policy": {
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"learning_rate": 0.0003,
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"learning_rate_decay": 1.0,
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"clip_ratio": 0.2,
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"iterations_per_epoch": 100,
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"hidden_layer_size": 20,
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"n_hidden_layers": 2,
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"activation": 0,
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"option": 0
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},
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"value": {
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"learning_rate": 0.001,
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"iterations_per_epoch": 1000
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},
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"discriminator": {
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"learning_rate": 0.001,
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"weight_decay": 0.0001,
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"iterations_per_epoch": 100,
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"n_hidden_layers_element": 3,
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"n_hidden_layers_global": 1,
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"hidden_layer_size": 10,
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"activation": 0
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},
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"train_epochs": 2,
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"seed": 0
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}
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33
best_configs/hail_expB.json
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33
best_configs/hail_expB.json
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{
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"experiment": "B",
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"trainenv": {
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"stop_on_collision": false,
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"safe_actions_collision_method": null,
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"abort_unsafe_collision_method": null
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},
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"policy": {
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"learning_rate": 0.0003,
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"learning_rate_decay": 1.0,
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"clip_ratio": 0.2,
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"iterations_per_epoch": 100,
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"hidden_layer_size": 20,
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"n_hidden_layers": 2,
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"activation": 0,
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"option": 0
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},
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"value": {
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"learning_rate": 0.001,
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"iterations_per_epoch": 1000
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},
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"discriminator": {
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"learning_rate": 0.001,
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"weight_decay": 0.0001,
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"iterations_per_epoch": 100,
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"n_hidden_layers_element": 3,
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"n_hidden_layers_global": 1,
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"hidden_layer_size": 10,
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"activation": 0
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},
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"train_epochs": 2,
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"seed": 0
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}
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33
best_configs/shail_expA.json
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33
best_configs/shail_expA.json
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@@ -0,0 +1,33 @@
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{
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"experiment": "A",
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"trainenv": {
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"stop_on_collision": false,
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"safe_actions_collision_method": "circle",
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"abort_unsafe_collision_method": "circle"
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},
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"policy": {
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"learning_rate": 0.0003,
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"learning_rate_decay": 1.0,
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"clip_ratio": 0.2,
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"iterations_per_epoch": 100,
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"hidden_layer_size": 20,
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"n_hidden_layers": 2,
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"activation": 0,
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"option": 0
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},
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"value": {
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"learning_rate": 0.001,
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"iterations_per_epoch": 1000
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},
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"discriminator": {
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"learning_rate": 0.001,
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"weight_decay": 0.0001,
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"iterations_per_epoch": 100,
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"n_hidden_layers_element": 3,
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"n_hidden_layers_global": 1,
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"hidden_layer_size": 10,
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"activation": 0
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},
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"train_epochs": 2,
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"seed": 0
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}
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33
best_configs/shail_expB.json
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33
best_configs/shail_expB.json
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{
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"experiment": "B",
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"trainenv": {
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"stop_on_collision": false,
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"safe_actions_collision_method": "circle",
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"abort_unsafe_collision_method": "circle"
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},
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"policy": {
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"learning_rate": 0.0003,
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"learning_rate_decay": 1.0,
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"clip_ratio": 0.2,
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"iterations_per_epoch": 100,
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"hidden_layer_size": 20,
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"n_hidden_layers": 2,
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"activation": 0,
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"option": 0
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},
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"value": {
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"learning_rate": 0.001,
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"iterations_per_epoch": 1000
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},
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"discriminator": {
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"learning_rate": 0.001,
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"weight_decay": 0.0001,
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"iterations_per_epoch": 100,
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"n_hidden_layers_element": 3,
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"n_hidden_layers_global": 1,
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"hidden_layer_size": 10,
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"activation": 0
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},
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"train_epochs": 2,
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"seed": 0
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}
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@@ -142,8 +142,8 @@ def training_function(config):
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v_opt=v_opt,
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v_iters=config['value']['iterations_per_epoch'],
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epochs=config['train_epochs'],
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rollout_episodes=60,
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rollout_steps=60,
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rollout_episodes=6, #60, FIXME
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rollout_steps=6, #60, FIXME
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gamma=0.99,
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gae_lambda=0.9,
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clip_ratio=config['policy']['clip_ratio'],
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@@ -162,44 +162,78 @@ if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('experiment', choices=['A', 'B'])
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parser.add_argument('--epochs', type=int, default=200)
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parser.add_argument('--test', type=str, help='path to config file to run final training on')
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parser.add_argument('--test_seeds', type=int, default=5)
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args = parser.parse_args()
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print('Running Tuning for Experiment %s'%(args.experiment))
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analysis = tune.run(
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training_function,
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config={
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'experiment': args.experiment,
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'trainenv': {
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'stop_on_collision': False,
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'safe_actions_collision_method': 'circle',
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'abort_unsafe_collision_method': 'circle',
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},
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'policy': {
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'learning_rate': 3e-4,
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'learning_rate_decay': 1.0,
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'clip_ratio': 0.2,
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'iterations_per_epoch': 100,
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'hidden_layer_size': tune.grid_search([20, 40]),
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'n_hidden_layers': tune.grid_search([2, 3]),
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'activation':0,
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'option': tune.grid_search(list(range(len(option_list))))
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},
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'value': {
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'learning_rate': 1e-3,
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'iterations_per_epoch': 1000,
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},
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'discriminator': {
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'learning_rate': 1e-3,
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'weight_decay': 1e-4,
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'iterations_per_epoch': 100,
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'n_hidden_layers_element': tune.grid_search([3,4]),
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'n_hidden_layers_global': tune.grid_search([1,2]),
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'hidden_layer_size': 10,
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'activation': 0,
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},
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'train_epochs': args.epochs,
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'seed': 0,
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}
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)
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print('Best config: ', analysis.get_best_config(metric='gen_collision_rate', mode='min'))
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# if no test config specified, train
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if args.test is None:
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print('Running Tuning for Experiment %s'%(args.experiment))
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analysis = tune.run(
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training_function,
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config={
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'experiment': args.experiment,
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'trainenv': {
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'stop_on_collision': False,
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'safe_actions_collision_method': 'circle',
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'abort_unsafe_collision_method': 'circle',
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},
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'policy': {
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'learning_rate': 3e-4,
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'learning_rate_decay': 1.0,
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'clip_ratio': 0.2,
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'iterations_per_epoch': 100,
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'hidden_layer_size': tune.grid_search([20, 40]),
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'n_hidden_layers': tune.grid_search([2, 3]),
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'activation':0,
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'option': tune.grid_search(list(range(len(option_list))))
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},
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'value': {
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'learning_rate': 1e-3,
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'iterations_per_epoch': 1000,
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},
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'discriminator': {
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'learning_rate': 1e-3,
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'weight_decay': 1e-4,
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'iterations_per_epoch': 100,
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'n_hidden_layers_element': tune.grid_search([3,4]),
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'n_hidden_layers_global': tune.grid_search([1,2]),
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'hidden_layer_size': 10,
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'activation': 0,
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},
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'train_epochs': args.epochs,
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'seed': 0,
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}
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)
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best_config = analysis.get_best_config(metric='gen_collision_rate', mode='min')
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print('Best config: ', best_config)
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# safe best_config
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if not os.path.isdir(os.path.join(DIR, 'best_configs')):
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os.mkdir(os.path.join(DIR, 'best_configs'))
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# save shail
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with open(os.path.join(DIR, 'best_configs',f'shail_exp{args.experiment}.json'), 'w', encoding='utf-8') as f:
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json.dump(best_config, f, ensure_ascii=False, indent=4)
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# save hail
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best_config['trainenv']['safe_actions_collision_method']=None
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best_config['trainenv']['abort_unsafe_collision_method']=None
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with open(os.path.join(DIR, 'best_configs',f'hail_exp{args.experiment}.json'), 'w', encoding='utf-8') as f:
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json.dump(best_config, f, ensure_ascii=False, indent=4)
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# if config file specified, rerun it with appropriate number of seeds
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else:
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with open(args.test, 'rb') as f:
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config = json.load(f)
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print(f'Retraining {args.test} with {args.test_seed} seeds on experiment {config["experiment"]}')
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# rerun with appropriate number of seeds
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config['seed'] = tune.grid_search(list(range(1,args.test_seeds+1)))
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analysis = tune.run(training_function, config)
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# move final policies to appropriate directory
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# import pdb
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# pdb.set_trace()
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# a = 0
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