227 lines
10 KiB
Python
227 lines
10 KiB
Python
import numpy as np
|
|
import torch
|
|
import argparse
|
|
import os
|
|
import gym
|
|
import time
|
|
import json
|
|
import dmc2gym
|
|
|
|
import wandb
|
|
import utils
|
|
from utils import ReplayBuffer, make_env, save_image
|
|
from models import ObservationEncoder, ObservationDecoder, TransitionModel, CLUBSample
|
|
from logger import Logger
|
|
from video import VideoRecorder
|
|
|
|
#from agent.baseline_agent import BaselineAgent
|
|
#from agent.bisim_agent import BisimAgent
|
|
#from agent.deepmdp_agent import DeepMDPAgent
|
|
#from agents.navigation.carla_env import CarlaEnv
|
|
|
|
|
|
def parse_args():
|
|
parser = argparse.ArgumentParser()
|
|
# environment
|
|
parser.add_argument('--domain_name', default='cheetah')
|
|
parser.add_argument('--task_name', default='run')
|
|
parser.add_argument('--image_size', default=84, type=int)
|
|
parser.add_argument('--channels', default=3, type=int)
|
|
parser.add_argument('--action_repeat', default=1, type=int)
|
|
parser.add_argument('--frame_stack', default=4, type=int)
|
|
parser.add_argument('--resource_files', type=str)
|
|
parser.add_argument('--eval_resource_files', type=str)
|
|
parser.add_argument('--img_source', default=None, type=str, choices=['color', 'noise', 'images', 'video', 'none'])
|
|
parser.add_argument('--total_frames', default=1000, type=int)
|
|
# replay buffer
|
|
parser.add_argument('--replay_buffer_capacity', default=50000, type=int) #100000
|
|
parser.add_argument('--episode_length', default=50, type=int)
|
|
# train
|
|
parser.add_argument('--agent', default='dpi', type=str, choices=['baseline', 'bisim', 'deepmdp', 'db', 'dpi', 'rpc'])
|
|
parser.add_argument('--init_steps', default=1000, type=int)
|
|
parser.add_argument('--num_train_steps', default=1000, type=int)
|
|
parser.add_argument('--batch_size', default=200, type=int) #512
|
|
parser.add_argument('--state_size', default=256, type=int)
|
|
parser.add_argument('--hidden_size', default=128, type=int)
|
|
parser.add_argument('--history_size', default=128, type=int)
|
|
parser.add_argument('--load_encoder', default=None, type=str)
|
|
parser.add_argument('--imagination_horizon', default=15, type=str)
|
|
# eval
|
|
parser.add_argument('--eval_freq', default=10, type=int) # TODO: master had 10000
|
|
parser.add_argument('--num_eval_episodes', default=20, type=int)
|
|
# critic
|
|
parser.add_argument('--critic_lr', default=1e-3, type=float)
|
|
parser.add_argument('--critic_beta', default=0.9, type=float)
|
|
parser.add_argument('--critic_tau', default=0.005, type=float)
|
|
parser.add_argument('--critic_target_update_freq', default=2, type=int)
|
|
# actor
|
|
parser.add_argument('--actor_lr', default=1e-3, type=float)
|
|
parser.add_argument('--actor_beta', default=0.9, type=float)
|
|
parser.add_argument('--actor_log_std_min', default=-10, type=float)
|
|
parser.add_argument('--actor_log_std_max', default=2, type=float)
|
|
parser.add_argument('--actor_update_freq', default=2, type=int)
|
|
# encoder/decoder
|
|
parser.add_argument('--encoder_type', default='pixel', type=str, choices=['pixel', 'pixelCarla096', 'pixelCarla098', 'identity'])
|
|
parser.add_argument('--encoder_feature_dim', default=50, type=int)
|
|
parser.add_argument('--encoder_lr', default=1e-3, type=float)
|
|
parser.add_argument('--encoder_tau', default=0.005, type=float)
|
|
parser.add_argument('--encoder_stride', default=1, type=int)
|
|
parser.add_argument('--decoder_type', default='pixel', type=str, choices=['pixel', 'identity', 'contrastive', 'reward', 'inverse', 'reconstruction'])
|
|
parser.add_argument('--decoder_lr', default=1e-3, type=float)
|
|
parser.add_argument('--decoder_update_freq', default=1, type=int)
|
|
parser.add_argument('--decoder_weight_lambda', default=0.0, type=float)
|
|
parser.add_argument('--num_layers', default=4, type=int)
|
|
parser.add_argument('--num_filters', default=32, type=int)
|
|
# sac
|
|
parser.add_argument('--discount', default=0.99, type=float)
|
|
parser.add_argument('--init_temperature', default=0.01, type=float)
|
|
parser.add_argument('--alpha_lr', default=1e-3, type=float)
|
|
parser.add_argument('--alpha_beta', default=0.9, type=float)
|
|
# misc
|
|
parser.add_argument('--seed', default=1, type=int)
|
|
parser.add_argument('--work_dir', default='.', type=str)
|
|
parser.add_argument('--save_tb', default=False, action='store_true')
|
|
parser.add_argument('--save_model', default=False, action='store_true')
|
|
parser.add_argument('--save_buffer', default=False, action='store_true')
|
|
parser.add_argument('--save_video', default=False, action='store_true')
|
|
parser.add_argument('--transition_model_type', default='', type=str, choices=['', 'deterministic', 'probabilistic', 'ensemble'])
|
|
parser.add_argument('--render', default=False, action='store_true')
|
|
parser.add_argument('--port', default=2000, type=int)
|
|
args = parser.parse_args()
|
|
return args
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
class DPI:
|
|
def __init__(self, args):
|
|
# wandb config
|
|
#run = wandb.init(project="dpi")
|
|
|
|
self.args = args
|
|
|
|
# environment setup
|
|
self.env = make_env(self.args)
|
|
self.env.seed(self.args.seed)
|
|
|
|
# stack several consecutive frames together
|
|
if self.args.encoder_type.startswith('pixel'):
|
|
self.env = utils.FrameStack(self.env, k=self.args.frame_stack)
|
|
|
|
# create replay buffer
|
|
self.data_buffer = ReplayBuffer(size=self.args.replay_buffer_capacity,
|
|
obs_shape=(self.args.frame_stack*self.args.channels,self.args.image_size,self.args.image_size),
|
|
action_size=self.env.action_space.shape[0],
|
|
seq_len=self.args.episode_length,
|
|
batch_size=args.batch_size,
|
|
args=self.args)
|
|
|
|
# create work directory
|
|
utils.make_dir(self.args.work_dir)
|
|
self.video_dir = utils.make_dir(os.path.join(self.args.work_dir, 'video'))
|
|
self.model_dir = utils.make_dir(os.path.join(self.args.work_dir, 'model'))
|
|
self.buffer_dir = utils.make_dir(os.path.join(self.args.work_dir, 'buffer'))
|
|
|
|
# create video recorder
|
|
#video = VideoRecorder(video_dir if args.save_video else None, resource_files=args.resource_files)
|
|
#video.init(enabled=True)
|
|
|
|
# create models
|
|
self.build_models(use_saved=False, saved_model_dir=self.model_dir)
|
|
|
|
def build_models(self, use_saved, saved_model_dir=None):
|
|
self.obs_encoder = ObservationEncoder(
|
|
obs_shape=(self.args.frame_stack*self.args.channels,self.args.image_size,self.args.image_size), # (12,84,84)
|
|
state_size=self.args.state_size # 128
|
|
)
|
|
|
|
self.obs_decoder = ObservationDecoder(
|
|
state_size=self.args.state_size, # 128
|
|
output_shape=(self.args.frame_stack*self.args.channels,self.args.image_size,self.args.image_size) # (12,84,84)
|
|
)
|
|
|
|
self.transition_model = TransitionModel(
|
|
state_size=self.args.state_size, # 128
|
|
hidden_size=self.args.hidden_size, # 256
|
|
action_size=self.env.action_space.shape[0], # 6
|
|
history_size=self.args.history_size, # 128
|
|
)
|
|
|
|
# model parameters
|
|
self.model_parameters = list(self.obs_encoder.parameters()) + list(self.obs_decoder.parameters()) + list(self.transition_model.parameters())
|
|
|
|
# optimizer
|
|
self.optimizer = torch.optim.Adam(self.model_parameters, lr=self.args.encoder_lr)
|
|
|
|
if use_saved:
|
|
self._use_saved_models(saved_model_dir)
|
|
|
|
def _use_saved_models(self, saved_model_dir):
|
|
self.obs_encoder.load_state_dict(torch.load(os.path.join(saved_model_dir, 'obs_encoder.pt')))
|
|
self.obs_decoder.load_state_dict(torch.load(os.path.join(saved_model_dir, 'obs_decoder.pt')))
|
|
self.transition_model.load_state_dict(torch.load(os.path.join(saved_model_dir, 'transition_model.pt')))
|
|
|
|
def collect_random_episodes(self, episodes):
|
|
obs = self.env.reset()
|
|
done = False
|
|
|
|
for episode_count in range(episodes):
|
|
video = VideoRecorder(self.video_dir if args.save_video else None, resource_files=args.resource_files)
|
|
video.init(enabled=True)
|
|
for i in range(self.args.episode_length):
|
|
action = self.env.action_space.sample()
|
|
next_obs, _, done, _ = self.env.step(action)
|
|
|
|
self.data_buffer.add(obs, action, next_obs, episode_count+1, done)
|
|
|
|
if args.save_video:
|
|
video.record(self.env)
|
|
|
|
if done:
|
|
obs = self.env.reset()
|
|
done=False
|
|
else:
|
|
obs = next_obs
|
|
video.save('%d.mp4' % episode_count)
|
|
print("Collected {} random episodes".format(episode_count+1))
|
|
#if args.save_video:
|
|
# video.record(self.env)
|
|
#video.save('%d.mp4' % step)
|
|
#video.close()
|
|
|
|
def train(self):
|
|
# collect experience
|
|
self.collect_random_episodes(self.args.batch_size)
|
|
|
|
# Group observations and next_observations by steps
|
|
observations = torch.Tensor(self.data_buffer.group_steps(self.data_buffer,"observations")).float()
|
|
next_observations = torch.Tensor(self.data_buffer.group_steps(self.data_buffer,"next_observations")).float()
|
|
|
|
# Train encoder
|
|
previous_information_loss = 0
|
|
for i in range(self.args.episode_length):
|
|
# Encode observations and next_observations
|
|
self.features = self.obs_encoder(observations[i]) # (N,128)
|
|
self.next_features = self.obs_encoder(next_observations[i]) # (N,128)
|
|
|
|
# Calculate upper bound loss
|
|
past_loss = previous_information_loss + self.upper_bound_minimization(self.features, self.next_features)
|
|
previous_information_loss = past_loss
|
|
print("past_loss: ", past_loss)
|
|
|
|
def upper_bound_minimization(self, features, next_features):
|
|
club_sample = CLUBSample(self.args.state_size,
|
|
self.args.state_size,
|
|
self.args.hidden_size)
|
|
club_loss = club_sample(features, next_features)
|
|
return club_loss
|
|
|
|
|
|
if __name__ == '__main__':
|
|
args = parse_args()
|
|
|
|
dpi = DPI(args)
|
|
dpi.train() |