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naming convention doesnt like env key, leakyrelu is no bueno, running quick experiment to see which option sets can work with our time left. the options that look 4s ahead are definitely out of the questions, possibly the 2s ones aswell

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Arec Jamgochian
2022-02-26 16:02:29 -08:00
parent 71e3c5f816
commit 3b9051505e
1 changed files with 18 additions and 18 deletions
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36
sgail-ppo-options-setobs2.py
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@@ -21,11 +21,10 @@ import json
DIR = os.path.dirname(os.path.abspath(__file__))
option_list = [[(vel, time) for vel in [0, 1, 2, 4, 6, 8, 10] for time in [5]],
[(vel, time) for vel in [0, 1, 2, 5, 7.5, 10] for time in [5, 20]],
[(vel, time) for vel in [0, 1, 2, 4, 6, 8, 10] for time in [5, 10, 20]], # was the best in training with single hidden layer, but very slow
[(vel, time) for vel in [0, 1, 2, 5, 7.5, 10] for time in [5, 20, 40]],
[(vel, time) for vel in [0, 2, 5, 10] for time in [5, 10, 20]],
[(vel, time) for vel in [0, 3, 10] for time in [5, 20, 40]]
[(vel, time) for vel in [0, 2.5, 5, 7.5, 10] for time in [5, 10]],
[(vel, time) for vel in [0, 2.5, 5, 7.5, 10] for time in [5, 20]],
[(vel, time) for vel in [0, 2.5, 5, 10] for time in [5, 10, 20]],
[(vel, time) for vel in [0, 3, 10] for time in [5, 10, 20]]
]
obs_min = np.array([
@@ -58,11 +57,12 @@ def training_function(config):
collision_penalty=0
),
check_collisions=True,
stop_on_collision=config['env']['stop_on_collision'], track=track,
stop_on_collision=config['trainenv']['stop_on_collision'], track=track,
), collision_distance=6, collision_penalty=100), lambda obs: (obs - obs_min) / (obs_max - obs_min + 1e-10))
), options=option_list[config['policy']['option']],
safe_actions_collision_method=config['env']['safe_actions_collision_method'],
abort_unsafe_collision_method=config['env']['abort_unsafe_collision_method']) for _ in range(20)] for track in range(4)],[])
), options=option_list[config['policy']['option']],
safe_actions_collision_method=config['trainenv']['safe_actions_collision_method'],
abort_unsafe_collision_method=config['trainenv']['abort_unsafe_collision_method'],
) for _ in range(20)] for track in range(4)],[])
env_fn = lambda i: envs[i]
@@ -118,9 +118,9 @@ def training_function(config):
value=value,
v_opt=v_opt,
v_iters=config['value']['iterations_per_epoch'],
epochs=301,
epochs=20, #300, #200 FIXME
rollout_episodes=60,
rollout_steps=60,
rollout_steps=60,
gamma=0.99,
gae_lambda=0.9,
clip_ratio=config['policy']['clip_ratio'],
@@ -137,19 +137,19 @@ def training_function(config):
analysis = tune.run(
training_function,
config={
'env': {
'stop_on_collision': False,
'safe_actions_collision_method': 'circle',
'abort_unsafe_collision_method': 'circle',
'trainenv': {
'stop_on_collision': False, #tune.grid_search([False, True]),
'safe_actions_collision_method': 'circle',
'abort_unsafe_collision_method': 'circle',
},
'policy': {
'learning_rate': 3e-4, # tune.grid_search([3e-4]),
'learning_rate_decay': 1.0, #tune.grid_search([1.0]),
'clip_ratio': 0.2, #tune.grid_search([0.2]),
'iterations_per_epoch': 100, #tune.grid_search([100]),
'hidden_layer_size': tune.grid_search([10, 20, 40]),
'n_hidden_layers': tune.grid_search([2, 3, 4]),
'activation':tune.grid_search([torch.nn.LeakyReLU, torch.nn.Tanh]),
'hidden_layer_size': 10, #tune.grid_search([10, 20, 40]), FIXME
'n_hidden_layers': 2, #tune.grid_search([2, 3, 4]), FIXME
'activation':torch.nn.Tanh,
'option': tune.grid_search(list(range(len(option_list))))
},
'value': {