Move and add scratch scripts
This commit is contained in:
Johannes Fischer
96
scratch/johannes/intersimple/gail_options_image.py
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96
scratch/johannes/intersimple/gail_options_image.py
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# %%
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import sys
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sys.path.append('../../../')
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from src.discriminator import CnnDiscriminator, CnnDiscriminatorFlatAction
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from imitation.algorithms import adversarial
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import stable_baselines3
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import torch.utils.data
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import numpy as np
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from intersim.envs.intersimple import NRasterized, speed_reward
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import itertools
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import functools
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from torch.distributions import Categorical
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import gym
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import torch
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import pickle
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import imitation.data.rollout as rollout
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import tempfile
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import pathlib
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from imitation.util import logger
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from stable_baselines3.common.env_util import make_vec_env
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from tqdm import tqdm
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from src.policies.options import OptionsCnnPolicy
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from src.gail.options import OptionsEnv, LLOptions, HLOptions, RenderOptions
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from src.gail.train import train_discriminator, train_generator
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from src.evaluation.evaluation import Evaluation
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model_name = 'gail_options_image'
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env_settings = {'agent': 51, 'width': 36, 'height': 36, 'm_per_px': 2}
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ALL_OPTIONS = [(v,t) for v in [0,2,4,6,8] for t in [5, 10]] # option 0 is safe fallback
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def train(expert_data, epochs=20, expert_batch_size=32, generator_steps=1024, discount=0.99):
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env = NRasterized(**env_settings)
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env.discount = discount
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tempdir = tempfile.TemporaryDirectory(prefix="quickstart")
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tempdir_path = pathlib.Path(tempdir.name)
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logger.configure(tempdir_path / "GAIL/")
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print(f"All Tensorboards and logging are being written inside {tempdir_path}/.")
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venv = make_vec_env(NRasterized, n_envs=1, env_kwargs=env_settings)
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discriminator = adversarial.GAIL(
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expert_data=expert_data,
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expert_batch_size=expert_batch_size,
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discrim_kwargs={'discrim_net': CnnDiscriminatorFlatAction(venv)},
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#discrim_kwargs={'discrim_net': CnnDiscriminator(venv)},
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venv=venv, # unused
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gen_algo=stable_baselines3.PPO("CnnPolicy", venv), # unused
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)
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generator = stable_baselines3.PPO(
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OptionsCnnPolicy,
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OptionsEnv(env, options=ALL_OPTIONS),
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verbose=1,
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n_steps=generator_steps,
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)
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# PPO.train requires logger as set up in
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# PPO._setup_learn (called by PPO.learn)
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generator._logger = stable_baselines3.common.utils.configure_logger(
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generator.verbose,
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generator.tensorboard_log,
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)
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for epoch in tqdm(range(epochs)):
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train_discriminator(LLOptions(env, options=ALL_OPTIONS), generator, discriminator, num_samples=expert_batch_size)
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train_generator(HLOptions(env, options=ALL_OPTIONS), generator, discriminator, num_samples=generator_steps)
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eval_env = env # possibly define eval env on different location
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ev = Evaluation(eval_env, n_eval_episodes=100)
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ev.evaluate(epoch, generator, discriminator, expert_data)
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return generator
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# %%
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if __name__ == '__main__':
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# %%
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with open("../../../scratch/etienne/intersimple/data/NormalizedIntersimpleExpertMu.001_NRasterizedInfoAgent51w36h36mppx2.pkl", "rb") as f:
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trajectories = pickle.load(f)
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transitions = rollout.flatten_trajectories(trajectories)
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generator = train(transitions)
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generator.save(model_name)
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# %%
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model = stable_baselines3.PPO.load(model_name)
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env = RenderOptions(NRasterized(**env_settings), options=ALL_OPTIONS)
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for s in env.sample_ll(model):
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if s['dones']:
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break
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env.close(filestr='render/'+model_name)
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91
scratch/johannes/intersimple/gail_options_image_random.py
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91
scratch/johannes/intersimple/gail_options_image_random.py
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# %%
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import sys
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sys.path.append('../../../')
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from src.discriminator import CnnDiscriminatorFlatAction
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from imitation.algorithms import adversarial
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import stable_baselines3
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import torch.utils.data
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import numpy as np
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from intersim.envs.intersimple import NRasterizedRandomAgent
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import itertools
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from torch.distributions import Categorical
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import gym
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import torch
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import pickle
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import imitation.data.rollout as rollout
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import tempfile
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import pathlib
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from imitation.util import logger
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from stable_baselines3.common.env_util import make_vec_env
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from tqdm import tqdm
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from src.policies.options import OptionsCnnPolicy
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from src.gail.options import OptionsEnv, LLOptions, HLOptions, RenderOptions
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from src.gail.train import train_discriminator, train_generator
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model_name = 'gail_options_image_random'
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env_settings = {'width': 36, 'height': 36, 'm_per_px': 2}
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ALL_OPTIONS = [(v,t) for v in [0,2,4,6,8] for t in [5, 10, 20]] # option 0 is safe fallback
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def train(expert_data, epochs=100, expert_batch_size=64, generator_steps=1024, discount=0.99):
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env = NRasterizedRandomAgent(**env_settings)
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env.discount = discount
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tempdir = tempfile.TemporaryDirectory(prefix="quickstart")
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tempdir_path = pathlib.Path(tempdir.name)
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logger.configure(tempdir_path / "GAIL/")
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print(f"All Tensorboards and logging are being written inside {tempdir_path}/.")
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venv = make_vec_env(NRasterizedRandomAgent, n_envs=1, env_kwargs=env_settings)
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discriminator = adversarial.GAIL(
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expert_data=expert_data,
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expert_batch_size=expert_batch_size,
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discrim_kwargs={'discrim_net': CnnDiscriminatorFlatAction(venv)},
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#discrim_kwargs={'discrim_net': CnnDiscriminator(venv)},
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venv=venv, # unused
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gen_algo=stable_baselines3.PPO("CnnPolicy", venv), # unused
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)
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generator = stable_baselines3.PPO(
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OptionsCnnPolicy,
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OptionsEnv(env, options=ALL_OPTIONS),
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verbose=1,
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n_steps=generator_steps,
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)
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# PPO.train requires logger as set up in
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# PPO._setup_learn (called by PPO.learn)
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generator._logger = stable_baselines3.common.utils.configure_logger(
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generator.verbose,
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generator.tensorboard_log,
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)
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for _ in tqdm(range(epochs)):
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train_discriminator(LLOptions(env, options=ALL_OPTIONS), generator, discriminator, num_samples=expert_batch_size)
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train_generator(HLOptions(env, options=ALL_OPTIONS), generator, discriminator, num_samples=generator_steps)
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generator.save(model_name)
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return generator
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def video(model_name, env):
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model = stable_baselines3.PPO.load(model_name)
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env = RenderOptions(env, options=ALL_OPTIONS)
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for s in env.sample_ll(model):
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if s['dones']:
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break
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env.close(filestr='render/'+model_name)
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def evaluate():
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video(
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model_name=model_name,
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env=NRasterizedRandomAgent(**env_settings)
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)
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# %%
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if __name__ == '__main__':
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with open("../../../scratch/etienne/intersimple/data/NormalizedIntersimpleExpertMu.001N10000_NRasterizedRandomAgentw36h36mppx2.pkl", "rb") as f:
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trajectories = pickle.load(f)
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transitions = rollout.flatten_trajectories(trajectories)
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train(transitions)
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100
scratch/johannes/intersimple/gail_options_image_random_eval.py
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100
scratch/johannes/intersimple/gail_options_image_random_eval.py
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@@ -0,0 +1,100 @@
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# %%
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import sys
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sys.path.append('../../../')
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from src.discriminator import CnnDiscriminatorFlatAction
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from imitation.algorithms import adversarial
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import stable_baselines3
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import torch.utils.data
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import numpy as np
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from intersim.envs.intersimple import NRasterizedRandomAgent
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import itertools
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from torch.distributions import Categorical
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import gym
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import torch
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import pickle
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import imitation.data.rollout as rollout
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import tempfile
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import pathlib
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from imitation.util import logger
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from stable_baselines3.common.env_util import make_vec_env
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from tqdm import tqdm
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from src.policies.options import OptionsCnnPolicy
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from src.gail.options import OptionsEnv, LLOptions, HLOptions, RenderOptions
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from src.gail.train import train_discriminator, train_generator
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from src.evaluation.evaluation import Evaluation
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from torch.utils.tensorboard import SummaryWriter
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model_name = 'gail_options_image_random'
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env_settings = {'width': 36, 'height': 36, 'm_per_px': 2}
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ALL_OPTIONS = [(v,t) for v in [0,2,4,6,8] for t in [5, 10, 20]] # option 0 is safe fallback
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def train(expert_data, epochs=100, expert_batch_size=64, generator_steps=1024, discount=0.99):
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env = NRasterizedRandomAgent(**env_settings)
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env.discount = discount
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tempdir = tempfile.TemporaryDirectory(prefix="quickstart")
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tempdir_path = pathlib.Path(tempdir.name)
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logger.configure(tempdir_path / "GAIL/")
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print(f"All Tensorboards and logging are being written inside {tempdir_path}/.")
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venv = make_vec_env(NRasterizedRandomAgent, n_envs=1, env_kwargs=env_settings)
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discriminator = adversarial.GAIL(
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expert_data=expert_data,
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expert_batch_size=expert_batch_size,
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discrim_kwargs={'discrim_net': CnnDiscriminatorFlatAction(venv)},
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#discrim_kwargs={'discrim_net': CnnDiscriminator(venv)},
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venv=venv, # unused
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gen_algo=stable_baselines3.PPO("CnnPolicy", venv), # unused
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)
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generator = stable_baselines3.PPO(
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OptionsCnnPolicy,
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OptionsEnv(env, options=ALL_OPTIONS),
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verbose=1,
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n_steps=generator_steps,
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)
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# PPO.train requires logger as set up in
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# PPO._setup_learn (called by PPO.learn)
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generator._logger = stable_baselines3.common.utils.configure_logger(
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generator.verbose,
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generator.tensorboard_log,
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)
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filestr = os.path.join('out', model_name)
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writer = SummaryWriter(filestr)
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ev = Evaluation(filestr, env, expert_data, n_eval_episodes=100)
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for epoch in tqdm(range(epochs)):
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train_discriminator(LLOptions(env, options=ALL_OPTIONS), generator, discriminator, num_samples=expert_batch_size)
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train_generator(HLOptions(env, options=ALL_OPTIONS), generator, discriminator, num_samples=generator_steps)
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generator.save(model_name)
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metrics = ev.evaluate(epoch, generator, discriminator, expert_data)
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for metric, value in metrics.items():
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writer.add_scalar(metric, value, epoch)
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return generator
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def video(model_name, env):
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model = stable_baselines3.PPO.load(model_name)
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env = RenderOptions(env, options=ALL_OPTIONS)
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for s in env.sample_ll(model):
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if s['dones']:
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break
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env.close(filestr='render/'+model_name)
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def evaluate():
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video(
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model_name=model_name,
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env=NRasterizedRandomAgent(**env_settings)
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)
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# %%
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if __name__ == '__main__':
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with open("../../../scratch/etienne/intersimple/data/NormalizedIntersimpleExpertMu.001N10000_NRasterizedRandomAgentInfow36h36mppx2.pkl", "rb") as f:
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trajectories = pickle.load(f)
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transitions = rollout.flatten_trajectories(trajectories)
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train(transitions)
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