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Use collision rate as main metric

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ebuehrle
2022-02-26 14:28:58 +01:00
parent 1e24612347
commit 35e6fb299c
2 changed files with 10 additions and 10 deletions
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12
sgail-ppo-options-setobs2.py
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@@ -92,20 +92,16 @@ def training_function(config):
expert_data = (torch.cat(d0), torch.cat(d1), torch.cat(d2), torch.cat(d3))
expert_data = Buffer(*expert_data)
run_folder = str(datetime.now())
os.mkdir(os.path.join(DIR, run_folder))
with open(os.path.join(DIR, run_folder, 'config.json'), 'w') as f:
json.dump(config, f, indent=4)
def callback(info):
tune.report(gen_mean_reward_per_episode=info['gen/mean_reward_per_episode'],
disc_mean_reward_per_episode=info['disc/mean_reward_per_episode'],
mean_episode_length=info['gen/mean_episode_length'])
mean_episode_length=info['gen/mean_episode_length'],
gen_collision_rate=info['gen/collision_rate'])
# save model checkpoints
ep = info['epoch'] + 1
if (ep % 25 == 0):
torch.save(info['policy'].state_dict(), os.path.join(DIR, run_folder, f'policy_epoch{ep}.pt'))
torch.save(info['policy'].state_dict(), f'policy_epoch{ep}.pt')
value, policy = gail_ppo(
env_fn=env_fn,
@@ -164,7 +160,7 @@ analysis = tune.run(
}
)
print('Best config: ', analysis.get_best_config(metric='gen_mean_reward_per_episode', mode='max'))
print('Best config: ', analysis.get_best_config(metric='gen_collision_rate', mode='min'))
# %%
# policy = SetMaskedDiscretePolicy(env_fn(0).action_space.n)