added ogail script and splitting up feasability on next line to avoid calculating it unnecessarily (though it might be fine as is)
This commit is contained in:
Arec Jamgochian
174
ogail-ppo-options-setobs2.py
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174
ogail-ppo-options-setobs2.py
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# %%
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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
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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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DIR = os.path.dirname(os.path.abspath(__file__))
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option_list = [[(vel, time) for vel in [0, 1, 2, 4, 6, 8, 10] for time in [5]],
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[(vel, time) for vel in [0, 1, 2, 5, 7.5, 10] for time in [5, 20]],
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[(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
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[(vel, time) for vel in [0, 1, 2, 5, 7.5, 10] for time in [5, 20, 40]],
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[(vel, time) for vel in [0, 2, 5, 10] for time in [5, 10, 20]],
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[(vel, time) for vel in [0, 3, 10] for time in [5, 20, 40]]
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]
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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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def training_function(config):
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np.random.seed(config['seed'])
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torch.manual_seed(config['seed'])
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envs = sum([[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=config['stop_on_collision'], track=track,
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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=option_list[config['policy']['option']], safe_actions_collision_method=None,
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abort_unsafe_collision_method=None) for _ in range(20)] for track in range(4)],[])
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env_fn = lambda i: envs[i]
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policy = SetMaskedDiscretePolicy(env_fn(0).action_space.n,
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n_hidden_layers=config['policy']['n_hidden_layers'],
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hidden_layer_size=config['policy']['hidden_layer_size'],
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activation=config['policy']['activation'] ) # 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 = [
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torch.load(os.path.join(DIR, 'intersimple-expert-data-setobs2-loc0-track0.pt')),
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torch.load(os.path.join(DIR, 'intersimple-expert-data-setobs2-loc0-track1.pt')),
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torch.load(os.path.join(DIR, 'intersimple-expert-data-setobs2-loc0-track2.pt')),
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torch.load(os.path.join(DIR, 'intersimple-expert-data-setobs2-loc0-track3.pt')),
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]
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d0 = [d[0] for d in expert_data]
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d1 = [d[1] for d in expert_data]
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d2 = [d[2] for d in expert_data]
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d3 = [d[3] for d in expert_data]
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expert_data = (torch.cat(d0), torch.cat(d1), torch.cat(d2), torch.cat(d3))
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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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disc_mean_reward_per_episode=info['disc/mean_reward_per_episode'],
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mean_episode_length=info['gen/mean_episode_length'])
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# save model checkpoints
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ep = info['epoch'] + 1
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if (ep % 25 == 0):
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torch.save(info['policy'].state_dict(), f'policy_epoch{ep}.pt')
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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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# save model
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torch.save(policy.state_dict(), 'policy_final.pt')
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analysis = tune.run(
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training_function,
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config={
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'stop_on_collision': tune.grid_search([True, False]),
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'policy': {
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'learning_rate': 3e-4, # tune.grid_search([3e-4]),
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'learning_rate_decay': 1.0, #tune.grid_search([1.0]),
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'clip_ratio': 0.2, #tune.grid_search([0.2]),
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'iterations_per_epoch': 100, #tune.grid_search([100]),
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'hidden_layer_size': tune.grid_search([10, 20, 40]),
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'n_hidden_layers': tune.grid_search([2, 3, 4]),
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'activation':tune.grid_search([torch.nn.LeakyReLU, torch.nn.Tanh]),
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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, # tune.grid_search([1e-3]),
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'iterations_per_epoch': 1000, #tune.grid_search([1000]),
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},
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'discriminator': {
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'learning_rate': 1e-3, #tune.grid_search([1e-3]),
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'weight_decay': 1e-4, #tune.grid_search([1e-4]),
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'iterations_per_epoch': 100, #tune.grid_search([100]),
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},
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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_mean_reward_per_episode', mode='max'))
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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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