132 lines
4.9 KiB
Python
132 lines
4.9 KiB
Python
# %%
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import os
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import gym
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from src.safe_options.options import gail_ppo, Buffer
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from src.core.value import SetValue
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from src.safe_options.policy import SetMaskedDiscretePolicy
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from src.core.discriminator import DeepsetDiscriminator
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import torch.optim
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from intersim.envs import IntersimpleLidarFlatRandom
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from intersim.envs.intersimple import speed_reward
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import functools
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from src.util.wrappers import CollisionPenaltyWrapper, TransformObservation, Setobs
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import numpy as np
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from src.safe_options.options import SafeOptionsEnv
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from torch.utils.tensorboard import SummaryWriter
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from ray import tune
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def training_function(config):
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obs_min = np.array([
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[-1000, -1000, 0, -np.pi, -1e-1, 0.],
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[0, -np.pi, -20, -20, -np.pi, -1e-1],
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[0, -np.pi, -20, -20, -np.pi, -1e-1],
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[0, -np.pi, -20, -20, -np.pi, -1e-1],
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[0, -np.pi, -20, -20, -np.pi, -1e-1],
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[0, -np.pi, -20, -20, -np.pi, -1e-1],
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]).reshape(-1)
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obs_max = np.array([
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[1000, 1000, 20, np.pi, 1e-1, 0.],
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[50, np.pi, 20, 20, np.pi, 1e-1],
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[50, np.pi, 20, 20, np.pi, 1e-1],
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[50, np.pi, 20, 20, np.pi, 1e-1],
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[50, np.pi, 20, 20, np.pi, 1e-1],
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[50, np.pi, 20, 20, np.pi, 1e-1],
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]).reshape(-1)
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envs = [SafeOptionsEnv(Setobs(
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TransformObservation(CollisionPenaltyWrapper(IntersimpleLidarFlatRandom(
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n_rays=5,
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reward=functools.partial(
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speed_reward,
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collision_penalty=0
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),
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stop_on_collision=True,
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), collision_distance=6, collision_penalty=100), lambda obs: (obs - obs_min) / (obs_max - obs_min + 1e-10))
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), 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)]
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env_fn = lambda i: envs[i]
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policy = SetMaskedDiscretePolicy(env_fn(0).action_space.n) # config net architecture
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pi_opt = torch.optim.Adam(policy.parameters(), lr=config['policy']['learning_rate'])
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pi_lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(pi_opt, gamma=config['policy']['learning_rate_decay'])
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value = SetValue() # config net architecture
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v_opt = torch.optim.Adam(value.parameters(), lr=config['value']['learning_rate'])
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discriminator = DeepsetDiscriminator() # config net architecture
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disc_opt = torch.optim.Adam(discriminator.parameters(), lr=config['discriminator']['learning_rate'], weight_decay=config['discriminator']['weight_decay'])
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expert_data = torch.load(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'intersimple-expert-data-setobs2.pt'))
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expert_data = Buffer(*expert_data)
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def callback(info):
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tune.report(gen_mean_reward_per_episode=info['gen/mean_reward_per_episode'])
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value, policy = gail_ppo(
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env_fn=env_fn,
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expert_data=expert_data,
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discriminator=discriminator,
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disc_opt=disc_opt,
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disc_iters=config['discriminator']['iterations_per_epoch'],
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policy=policy,
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value=value,
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v_opt=v_opt,
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v_iters=config['value']['iterations_per_epoch'],
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epochs=200,
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rollout_episodes=60,
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rollout_steps=60,
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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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pi_opt=pi_opt,
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pi_iters=config['policy']['iterations_per_epoch'],
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logger=SummaryWriter(comment='sgail-ppo-options-setobs2'),
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callback=callback,
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lr_schedulers=[pi_lr_scheduler],
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)
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analysis = tune.run(
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training_function,
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config={
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'policy': {
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'learning_rate': tune.grid_search([3e-4]),
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'learning_rate_decay': tune.grid_search([1.0]),
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'clip_ratio': tune.grid_search([0.2]),
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'iterations_per_epoch': tune.grid_search([100]),
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},
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'value': {
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'learning_rate': tune.grid_search([1e-3]),
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'iterations_per_epoch': tune.grid_search([1000]),
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},
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'discriminator': {
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'learning_rate': tune.grid_search([1e-3]),
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'weight_decay': tune.grid_search([1e-4]),
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'iterations_per_epoch': tune.grid_search([100]),
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}
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}
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)
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print('Best config: ', analysis.get_best_config(metric='gen_mean_reward_per_episode', mode='min'))
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# %%
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# policy = SetMaskedDiscretePolicy(env_fn(0).action_space.n)
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# policy(torch.zeros(env_fn(0).observation_space['observation'].shape), torch.zeros(env_fn(0).observation_space['safe_actions'].shape))
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# policy.load_state_dict(torch.load('sgail-ppo-options-setobs2.pt'))
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# env = env_fn(0)
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# obs = env.reset()
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# env.render(mode='post')
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# for i in range(300):
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# action = policy.sample(policy(
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# torch.tensor(obs['observation'], dtype=torch.float32),
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# torch.tensor(obs['safe_actions'], dtype=torch.float32),
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# ))
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# obs, reward, done, _ = env.step(action, render_mode='post')
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# print('step', i, 'reward', reward, 'safe actions', obs['safe_actions'])
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# if done:
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# break
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# env.close()
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# %%
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