Add tuning script for GAIL (PPO)
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ebuehrle
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gail-experiment.py
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240
gail-experiment.py
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# %%
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import os
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import gym
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from src.core.gail import gail_ppo, Buffer
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from src.core.value import SetValue
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from src.core.policy import SetPolicy
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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 torch.utils.tensorboard import SummaryWriter
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from ray import tune
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from datetime import datetime
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import json
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DIR = os.path.dirname(os.path.abspath(__file__))
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activations = [torch.nn.Tanh, torch.nn.LeakyReLU]
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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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if config['experiment'] == 'A':
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envs = [Setobs(TransformObservation(CollisionPenaltyWrapper(
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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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check_collisions=True,
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stop_on_collision=config['trainenv']['stop_on_collision'],
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), collision_distance=6, collision_penalty=100),
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lambda obs: (obs - obs_min) / (obs_max - obs_min + 1e-10)
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)) for _ in range(60)]
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elif config['experiment'] == 'B':
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envs = sum([[Setobs(TransformObservation(CollisionPenaltyWrapper(
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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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check_collisions=True,
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stop_on_collision=config['trainenv']['stop_on_collision'],
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track=track,
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), collision_distance=6, collision_penalty=100),
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lambda obs: (obs - obs_min) / (obs_max - obs_min + 1e-10)
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)) for _ in range(15)] for track in range(4)],[])
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else:
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raise NotImplementedError
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env_fn = lambda i: envs[i]
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policy = SetPolicy(env_fn(0).action_space.shape[0],
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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=activations[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'], weight_decay=config['value']['weight_decay'])
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discriminator = DeepsetDiscriminator(
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n_hidden_layers_element=config['discriminator']['n_hidden_layers_element'],
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n_hidden_layers_global=config['discriminator']['n_hidden_layers_global'],
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hidden_layer_size=config['discriminator']['hidden_layer_size'],
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activation=activations[config['discriminator']['activation']],
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)
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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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if config['experiment'] == 'A':
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expert_data = torch.load(os.path.join(DIR, 'intersimple-expert-data-setobs2-loc0-track0.pt'))
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elif config['experiment'] == 'B':
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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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gen_collision_rate=info['gen/collision_rate'])
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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=config['train_epochs'],
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rollout_episodes=60,
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rollout_steps=200,
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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='gail-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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if __name__ == '__main__':
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import argparse
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parser = argparse.ArgumentParser()
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parser.add_argument('--train', choices=['A', 'B'])
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parser.add_argument('--epochs', type=int, default=200)
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parser.add_argument('--test', type=str, help='path to config file to run final training on')
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parser.add_argument('--test_seeds', type=int, default=5)
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parser.add_argument('--test_cpus', type=int, help='number of cpus available to split test seed training over')
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args = parser.parse_args()
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assert (args.train is None) ^ (args.test is None), 'Must either train on an experiment or test with a config file'
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# if no test config specified, train
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if args.test is None:
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print('Running Tuning for Experiment %s'%(args.train))
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analysis = tune.run(
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training_function,
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config={
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'experiment': args.train,
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'trainenv': {
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'stop_on_collision': False,
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'safe_actions_collision_method': 'circle',
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'abort_unsafe_collision_method': 'circle',
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},
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'policy': {
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'learning_rate': 3e-4,
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'learning_rate_decay': 1.0,
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'clip_ratio': 0.2,
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'iterations_per_epoch': 100,
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'hidden_layer_size': tune.grid_search([20, 40]),
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'n_hidden_layers': tune.grid_search([2, 3]),
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'activation':0,
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},
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'value': {
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'learning_rate': 1e-4,
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'weight_decay': 1e-3,
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'iterations_per_epoch': 1000,
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},
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'discriminator': {
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'learning_rate': 1e-3,
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'weight_decay': 1e-5,
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'iterations_per_epoch': 500,
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'n_hidden_layers_element': tune.grid_search([3,4]),
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'n_hidden_layers_global': tune.grid_search([1,2]),
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'hidden_layer_size': 10,
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'activation': 0,
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},
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'train_epochs': args.epochs,
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'seed': 0,
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}
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)
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best_config = analysis.get_best_config(metric='gen_collision_rate', mode='min')
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print('Best config: ', best_config)
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# safe best_config
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if not os.path.isdir(os.path.join(DIR, 'best_configs')):
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os.mkdir(os.path.join(DIR, 'best_configs'))
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# save gail
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with open(os.path.join(DIR, 'best_configs',f'gail_exp{args.train}.json'), 'w', encoding='utf-8') as f:
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json.dump(best_config, f, ensure_ascii=False, indent=4)
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# if config file specified, rerun it with appropriate number of seeds
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else:
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with open(args.test, 'rb') as f:
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config = json.load(f)
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print(f'Retraining {args.test} with {args.test_seeds} seeds on experiment {config["experiment"]}')
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# rerun with appropriate number of seeds
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rpt = {'cpu': int(args.test_cpus/args.test_seeds)} if (args.test_cpus is not None) else None
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config['seed'] = tune.grid_search(list(range(1,args.test_seeds+1)))
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analysis = tune.run(training_function, config=config, resources_per_trial=rpt)
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# move final policies to appropriate directory
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split_ = os.path.basename(args.test).split('_')
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model = split_[0]
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exper = split_[-1].split('.')[0]
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savepath = os.path.join('test_policies',model,exper)
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if not os.path.isdir(savepath):
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os.makedirs(savepath)
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import shutil
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for i in range(args.test_seeds):
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s = analysis._checkpoints[i]['config']['seed']
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check_dir = analysis._checkpoints[i]['logdir']
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shutil.copyfile(os.path.join(check_dir,'policy_final.pt'),
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os.path.join(savepath, f'policy_seed{s}.pt'))
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