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Incrementing agent expert data, smaller policy network

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ebuehrle
2022-02-25 01:23:15 +01:00
parent 0d193d4af3
commit 02c1813b00
7 changed files with 29 additions and 13 deletions
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24
sgail-ppo-options-setobs2.py
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@@ -42,7 +42,7 @@ def training_function(config):
speed_reward,
collision_penalty=0
),
stop_on_collision=True,
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)]
@@ -58,7 +58,17 @@ def training_function(config):
discriminator = DeepsetDiscriminator() # config net architecture
disc_opt = torch.optim.Adam(discriminator.parameters(), lr=config['discriminator']['learning_rate'], weight_decay=config['discriminator']['weight_decay'])
expert_data = torch.load(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'intersimple-expert-data-setobs2.pt'))
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(os.path.dirname(os.path.abspath(__file__)), '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(os.path.dirname(os.path.abspath(__file__)), 'intersimple-expert-data-setobs2-loc0-track3.pt')),
]
d0 = [d[0] for d in expert_data]
d1 = [d[1] for d in expert_data]
d2 = [d[2] for d in expert_data]
d3 = [d[3] for d in expert_data]
expert_data = (torch.cat(d0), torch.cat(d1), torch.cat(d2), torch.cat(d3))
expert_data = Buffer(*expert_data)
def callback(info):
@@ -91,11 +101,11 @@ analysis = tune.run(
training_function,
config={
'policy': {
'learning_rate': tune.grid_search([1e-5, 7e-5, 3e-4]),
'learning_rate_decay': tune.grid_search([1.0, 0.98]),
'clip_ratio': tune.grid_search([0.2, 0.1]),
'learning_rate': tune.grid_search([3e-4]),
'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, 25, 50])
'hidden_layer_size': tune.grid_search([10])
},
'value': {
'learning_rate': tune.grid_search([1e-3]),
@@ -109,7 +119,7 @@ analysis = tune.run(
}
)
print('Best config: ', analysis.get_best_config(metric='gen_mean_reward_per_episode', mode='min'))
print('Best config: ', analysis.get_best_config(metric='gen_mean_reward_per_episode', mode='max'))
# %%
# policy = SetMaskedDiscretePolicy(env_fn(0).action_space.n)