648 lines
33 KiB
Python
648 lines
33 KiB
Python
import os
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import gc
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import copy
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import tqdm
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import wandb
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import random
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import argparse
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import numpy as np
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import utils
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from utils import ReplayBuffer, FreezeParameters, make_env, preprocess_obs, soft_update_params, save_image
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from models import ObservationEncoder, ObservationDecoder, TransitionModel, Actor, ValueModel, RewardModel, ProjectionHead, ContrastiveHead, CLUBSample
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from logger import Logger
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from video import VideoRecorder
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from dmc2gym.wrappers import set_global_var
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torchvision.transforms as T
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from torch.utils.tensorboard import SummaryWriter
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#from agent.baseline_agent import BaselineAgent
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#from agent.bisim_agent import BisimAgent
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#from agent.deepmdp_agent import DeepMDPAgent
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#from agents.navigation.carla_env import CarlaEnv
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def parse_args():
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parser = argparse.ArgumentParser()
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# environment
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parser.add_argument('--domain_name', default='cheetah')
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parser.add_argument('--version', default=1, type=int)
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parser.add_argument('--task_name', default='run')
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parser.add_argument('--image_size', default=84, type=int)
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parser.add_argument('--channels', default=3, type=int)
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parser.add_argument('--action_repeat', default=2, type=int)
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parser.add_argument('--frame_stack', default=3, type=int)
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parser.add_argument('--collection_interval', default=100, type=int)
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parser.add_argument('--resource_files', type=str)
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parser.add_argument('--eval_resource_files', type=str)
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parser.add_argument('--img_source', default=None, type=str, choices=['color', 'noise', 'images', 'video', 'none'])
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parser.add_argument('--total_frames', default=1000, type=int) # 10000
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parser.add_argument('--high_noise', action='store_true')
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# replay buffer
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parser.add_argument('--replay_buffer_capacity', default=50000, type=int) #50000
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parser.add_argument('--episode_length', default=21, type=int)
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# train
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parser.add_argument('--agent', default='dpi', type=str, choices=['baseline', 'bisim', 'deepmdp', 'db', 'dpi', 'rpc'])
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parser.add_argument('--init_steps', default=10000, type=int)
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parser.add_argument('--num_train_steps', default=100000, type=int)
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parser.add_argument('--batch_size', default=128, type=int) #512
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parser.add_argument('--state_size', default=30, type=int)
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parser.add_argument('--hidden_size', default=256, type=int)
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parser.add_argument('--history_size', default=128, type=int)
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parser.add_argument('--episode_collection', default=5, type=int)
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parser.add_argument('--episodes_buffer', default=20, type=int)
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parser.add_argument('--num-units', type=int, default=50, help='num hidden units for reward/value/discount models')
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parser.add_argument('--load_encoder', default=None, type=str)
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parser.add_argument('--imagine_horizon', default=10, type=str)
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parser.add_argument('--grad_clip_norm', type=float, default=100.0, help='Gradient clipping norm')
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# eval
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parser.add_argument('--eval_freq', default=10, type=int) # TODO: master had 10000
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parser.add_argument('--num_eval_episodes', default=20, type=int)
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parser.add_argument('--evaluation_interval', default=10000, type=int) # TODO: master had 10000
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# value
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parser.add_argument('--value_lr', default=8e-6, type=float)
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parser.add_argument('--value_beta', default=0.9, type=float)
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parser.add_argument('--value_tau', default=0.005, type=float)
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parser.add_argument('--value_target_update_freq', default=100, type=int)
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parser.add_argument('--td_lambda', default=0.95, type=int)
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# actor
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parser.add_argument('--actor_lr', default=8e-6, type=float)
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parser.add_argument('--actor_beta', default=0.9, type=float)
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parser.add_argument('--actor_log_std_min', default=-10, type=float)
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parser.add_argument('--actor_log_std_max', default=2, type=float)
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parser.add_argument('--actor_update_freq', default=2, type=int)
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# world/encoder/decoder
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parser.add_argument('--encoder_type', default='pixel', type=str, choices=['pixel', 'pixelCarla096', 'pixelCarla098', 'identity'])
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parser.add_argument('--world_model_lr', default=1e-5, type=float)
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parser.add_argument('--encoder_tau', default=0.005, type=float)
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parser.add_argument('--decoder_type', default='pixel', type=str, choices=['pixel', 'identity', 'contrastive', 'reward', 'inverse', 'reconstruction'])
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parser.add_argument('--num_layers', default=4, type=int)
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parser.add_argument('--num_filters', default=32, type=int)
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parser.add_argument('--aug', action='store_true')
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# sac
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parser.add_argument('--discount', default=0.99, type=float)
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# misc
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parser.add_argument('--seed', default=1, type=int)
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parser.add_argument('--logging_freq', default=100, type=int)
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parser.add_argument('--saving_interval', default=2500, type=int)
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parser.add_argument('--work_dir', default='.', type=str)
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parser.add_argument('--save_tb', default=False, action='store_true')
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parser.add_argument('--save_model', default=False, action='store_true')
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parser.add_argument('--save_buffer', default=False, action='store_true')
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parser.add_argument('--save_video', default=False, action='store_true')
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parser.add_argument('--transition_model_type', default='', type=str, choices=['', 'deterministic', 'probabilistic', 'ensemble'])
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parser.add_argument('--render', default=False, action='store_true')
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args = parser.parse_args()
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return args
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class DPI:
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def __init__(self, args):
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# wandb config
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#run = wandb.init(project="dpi")
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self.args = args
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# set environment noise
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set_global_var(self.args.high_noise)
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# environment setup
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self.env = make_env(self.args)
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#self.args.seed = np.random.randint(0, 1000)
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self.env.seed(self.args.seed)
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self.global_episodes = 0
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# noiseless environment setup
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self.args.version = 2 # env_id changes to v2
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self.args.img_source = None # no image noise
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self.args.resource_files = None
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# stack several consecutive frames together
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if self.args.encoder_type.startswith('pixel'):
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self.env = utils.FrameStack(self.env, k=self.args.frame_stack)
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self.env = utils.ActionRepeat(self.env, self.args.action_repeat)
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self.env = utils.NormalizeActions(self.env)
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# create replay buffer
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self.data_buffer = ReplayBuffer(size=self.args.replay_buffer_capacity,
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obs_shape=(self.args.frame_stack*self.args.channels,self.args.image_size,self.args.image_size),
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action_size=self.env.action_space.shape[0],
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seq_len=self.args.episode_length,
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batch_size=args.batch_size,
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args=self.args)
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# create work directory
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utils.make_dir(self.args.work_dir)
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self.video_dir = utils.make_dir(os.path.join(self.args.work_dir, 'video'))
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self.model_dir = utils.make_dir(os.path.join(self.args.work_dir, 'model'))
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self.buffer_dir = utils.make_dir(os.path.join(self.args.work_dir, 'buffer'))
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# create models
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self.build_models(use_saved=False, saved_model_dir=self.model_dir)
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def build_models(self, use_saved, saved_model_dir=None):
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# World Models
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self.obs_encoder = ObservationEncoder(
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obs_shape=(self.args.frame_stack*self.args.channels,self.args.image_size,self.args.image_size), # (9,84,84)
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state_size=self.args.state_size # 128
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).to(device)
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self.obs_encoder_momentum = ObservationEncoder(
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obs_shape=(self.args.frame_stack*self.args.channels,self.args.image_size,self.args.image_size), # (9,84,84)
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state_size=self.args.state_size # 128
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).to(device)
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self.obs_decoder = ObservationDecoder(
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state_size=self.args.state_size, # 128
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output_shape=(self.args.channels*self.args.channels,self.args.image_size,self.args.image_size) # (3,84,84)
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).to(device)
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self.transition_model = TransitionModel(
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state_size=self.args.state_size, # 128
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hidden_size=self.args.hidden_size, # 256
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action_size=self.env.action_space.shape[0], # 6
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history_size=self.args.history_size, # 128
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).to(device)
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# Actor Model
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self.actor_model = Actor(
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state_size=self.args.state_size, # 128
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hidden_size=self.args.hidden_size, # 256,
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action_size=self.env.action_space.shape[0], # 6
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).to(device)
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self.actor_model.apply(self.init_weights)
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# Value Models
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self.value_model = ValueModel(
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state_size=self.args.state_size, # 128
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hidden_size=self.args.hidden_size, # 256
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).to(device)
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self.target_value_model = ValueModel(
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state_size=self.args.state_size, # 128
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hidden_size=self.args.hidden_size, # 256
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).to(device)
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self.reward_model = RewardModel(
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state_size=self.args.state_size, # 128
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hidden_size=self.args.hidden_size, # 256
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).to(device)
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# Contrastive Models
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self.prjoection_head = ProjectionHead(
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state_size=self.args.state_size, # 128
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action_size=self.env.action_space.shape[0], # 6
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hidden_size=self.args.hidden_size, # 256
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).to(device)
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self.prjoection_head_momentum = ProjectionHead(
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state_size=self.args.state_size, # 128
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action_size=self.env.action_space.shape[0], # 6
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hidden_size=self.args.hidden_size, # 256
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).to(device)
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self.contrastive_head = ContrastiveHead(
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hidden_size=self.args.hidden_size, # 256
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).to(device)
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self.club_sample = CLUBSample(
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x_dim=self.args.state_size, # 128
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y_dim=self.args.state_size, # 128
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hidden_size=self.args.hidden_size, # 256
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).to(device)
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# model parameters
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self.world_model_parameters = list(self.obs_encoder.parameters()) + list(self.prjoection_head.parameters()) + \
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list(self.transition_model.parameters()) + list(self.obs_decoder.parameters()) + \
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list(self.reward_model.parameters()) + list(self.club_sample.parameters())
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self.past_transition_parameters = self.transition_model.parameters()
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# optimizers
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self.world_model_opt = torch.optim.Adam(self.world_model_parameters, self.args.world_model_lr)
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self.value_opt = torch.optim.Adam(self.value_model.parameters(), self.args.value_lr)
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self.actor_opt = torch.optim.Adam(self.actor_model.parameters(), self.args.actor_lr)
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#self.reward_opt = torch.optim.Adam(self.reward_model.parameters(), 1e-5)
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#self.decoder_opt = torch.optim.Adam(self.obs_decoder.parameters(), 1e-4)
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# Create Modules
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self.world_model_modules = [self.obs_encoder, self.prjoection_head, self.transition_model, self.obs_decoder, self.reward_model, self.club_sample]
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self.value_modules = [self.value_model]
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self.actor_modules = [self.actor_model]
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#self.reward_modules = [self.reward_model]
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#self.decoder_modules = [self.obs_decoder]
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if use_saved:
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self._use_saved_models(saved_model_dir)
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def _use_saved_models(self, saved_model_dir):
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self.obs_encoder.load_state_dict(torch.load(os.path.join(saved_model_dir, 'obs_encoder.pt')))
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self.obs_decoder.load_state_dict(torch.load(os.path.join(saved_model_dir, 'obs_decoder.pt')))
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self.transition_model.load_state_dict(torch.load(os.path.join(saved_model_dir, 'transition_model.pt')))
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def collect_sequences(self, episodes, random=True, actor_model=None, encoder_model=None):
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obs = self.env.reset()
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done = False
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all_rews = []
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for episode_count in tqdm.tqdm(range(episodes), desc='Collecting episodes'):
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self.global_episodes += 1
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epi_reward = 0
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while not done:
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if random:
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action = self.env.action_space.sample()
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else:
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with torch.no_grad():
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obs = torch.tensor(obs.copy(), dtype=torch.float32).unsqueeze(0)
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obs_processed = preprocess_obs(obs).to(device)
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state = self.obs_encoder(obs_processed)["distribution"].sample()
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action = self.actor_model(state).cpu().numpy().squeeze()
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#action = self.env.action_space.sample()
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next_obs, rew, done, _ = self.env.step(action)
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self.data_buffer.add(obs, action, next_obs, rew, done, self.global_episodes)
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obs = next_obs
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epi_reward += rew
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obs = self.env.reset()
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done=False
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all_rews.append(epi_reward)
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return all_rews
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def train(self, step, total_steps):
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counter = 0
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import matplotlib.pyplot as plt
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fig, ax = plt.subplots()
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while step < total_steps:
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# collect experience
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if step !=0:
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encoder = self.obs_encoder
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actor = self.actor_model
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all_rews = self.collect_sequences(self.args.episode_collection, random=False, actor_model=actor, encoder_model=encoder)
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else:
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all_rews = self.collect_sequences(self.args.episodes_buffer, random=True)
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# collect sequences
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non_zero_indices = np.nonzero(self.data_buffer.episode_count)[0]
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current_obs = self.data_buffer.observations[non_zero_indices]
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next_obs = self.data_buffer.next_observations[non_zero_indices]
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actions_raw = self.data_buffer.actions[non_zero_indices]
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rewards = self.data_buffer.rewards[non_zero_indices]
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self.terms = np.where(self.data_buffer.terminals[non_zero_indices]!=0)[0]
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# Group by episodes
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current_obs = self.grouped_arrays(current_obs)
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next_obs = self.grouped_arrays(next_obs)
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actions_raw = self.grouped_arrays(actions_raw)
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rewards_ = self.grouped_arrays(rewards)
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# Train encoder
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if step == 0:
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step += 1
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update_steps = 1 if step > 1 else 1
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#for _ in range(self.args.collection_interval // self.args.episode_length+1):
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for _ in range(update_steps):
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counter += 1
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# Select random chunks of episodes
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if current_obs.shape[0] < self.args.batch_size:
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random_episode_number = np.random.randint(0, current_obs.shape[0], self.args.batch_size)
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else:
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random_episode_number = random.sample(range(current_obs.shape[0]), self.args.batch_size)
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if current_obs[0].shape[0]-self.args.episode_length < self.args.batch_size:
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init_index = np.random.randint(0, current_obs[0].shape[0]-self.args.episode_length-2, self.args.batch_size)
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else:
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init_index = np.asarray(random.sample(range(current_obs[0].shape[0]-self.args.episode_length), self.args.batch_size))
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random.shuffle(random_episode_number)
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random.shuffle(init_index)
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last_observations = self.select_first_k(current_obs, init_index, random_episode_number)[:-1]
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current_observations = self.select_first_k(current_obs, init_index, random_episode_number)[1:]
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next_observations = self.select_first_k(next_obs, init_index, random_episode_number)[:-1]
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actions = self.select_first_k(actions_raw, init_index, random_episode_number)[:-1].to(device)
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next_actions = self.select_first_k(actions_raw, init_index, random_episode_number)[1:].to(device)
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rewards = self.select_first_k(rewards_, init_index, random_episode_number)[1:].to(device)
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# Preprocessing
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last_observations = preprocess_obs(last_observations).to(device)
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current_observations = preprocess_obs(current_observations).to(device)
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next_observations = preprocess_obs(next_observations).to(device)
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# Initialize transition model states
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self.transition_model.init_states(self.args.batch_size, device) # (N,128)
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self.history = self.transition_model.prev_history # (N,128)
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past_world_model_loss = 0
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past_action_loss = 0
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past_value_loss = 0
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for i in range(self.args.episode_length-1):
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if i > 0:
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# Encode observations and next_observations
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self.last_states_dict = self.get_features(last_observations[i])
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self.current_states_dict = self.get_features(current_observations[i])
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self.next_states_dict = self.get_features(next_observations[i], momentum=True)
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self.action = actions[i] # (N,6)
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self.next_action = next_actions[i] # (N,6)
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history = self.transition_model.prev_history
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# Encode negative observations fro upper bound loss
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idx = torch.randperm(current_observations[i].shape[0]) # random permutation on batch
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random_time_index = torch.randint(0, current_observations.shape[0]-2, (1,)).item() # random time index
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negative_current_observations = current_observations[random_time_index][idx]
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self.negative_current_states_dict = self.get_features(negative_current_observations)
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# Predict current state from past state with transition model
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last_states_sample = self.last_states_dict["sample"]
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predicted_current_state_dict = self.transition_model.imagine_step(last_states_sample, self.action, self.history)
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self.history = predicted_current_state_dict["history"]
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# Calculate upper bound loss
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likeli_loss, ub_loss = self._upper_bound_minimization(self.last_states_dict["sample"].detach(),
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self.current_states_dict["sample"].detach(),
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self.negative_current_states_dict["sample"].detach(),
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predicted_current_state_dict["sample"].detach(),
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)
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# Calculate encoder loss
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encoder_loss = self._past_encoder_loss(self.current_states_dict, predicted_current_state_dict)
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# decoder loss
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horizon = np.minimum(self.args.imagine_horizon, self.args.episode_length-1-i)
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nxt_obs = next_observations[i:i+horizon].reshape(-1,9,84,84)
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next_states_encodings = self.get_features(nxt_obs)["sample"].view(horizon,self.args.batch_size, -1)
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obs_dist = self.obs_decoder(next_states_encodings)
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decoder_loss = -torch.mean(obs_dist.log_prob(next_observations[i:i+horizon]))
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# contrastive projection
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vec_anchor = predicted_current_state_dict["sample"].detach()
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vec_positive = self.next_states_dict["sample"].detach()
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z_anchor = self.prjoection_head(vec_anchor, self.action)
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z_positive = self.prjoection_head_momentum(vec_positive, next_actions[i]).detach()
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# contrastive loss
|
|
logits = self.contrastive_head(z_anchor, z_positive)
|
|
labels = torch.arange(logits.shape[0]).long().to(device)
|
|
lb_loss = F.cross_entropy(logits, labels)
|
|
|
|
# reward loss
|
|
reward_dist = self.reward_model(self.current_states_dict["sample"])
|
|
reward_loss = -torch.mean(reward_dist.log_prob(rewards[i]))
|
|
|
|
# world model loss
|
|
world_model_loss = (10*encoder_loss + 10*ub_loss + 1e-1*lb_loss + reward_loss + 1e-3*decoder_loss + past_world_model_loss) * 1e-3
|
|
past_world_model_loss = world_model_loss.item()
|
|
|
|
# actor loss
|
|
with FreezeParameters(self.world_model_modules):
|
|
imagine_horizon = self.args.imagine_horizon #np.minimum(self.args.imagine_horizon, self.args.episode_length-1-i)
|
|
action = self.actor_model(self.current_states_dict["sample"])
|
|
imagined_rollout = self.transition_model.imagine_rollout(self.current_states_dict["sample"],
|
|
action, self.history,
|
|
imagine_horizon)
|
|
|
|
with FreezeParameters(self.world_model_modules + self.value_modules):
|
|
imag_rewards = self.reward_model(imagined_rollout["sample"]).mean
|
|
imag_values = self.target_value_model(imagined_rollout["sample"]).mean
|
|
|
|
discounts = self.args.discount * torch.ones_like(imag_rewards).detach()
|
|
|
|
self.returns = self._compute_lambda_return(imag_rewards[:-1],
|
|
imag_values[:-1],
|
|
discounts[:-1] ,
|
|
self.args.td_lambda,
|
|
imag_values[-1])
|
|
|
|
discounts = torch.cat([torch.ones_like(discounts[:1]), discounts[1:-1]], 0)
|
|
self.discounts = torch.cumprod(discounts, 0).detach()
|
|
actor_loss = -torch.mean(self.discounts * self.returns) + past_action_loss
|
|
past_action_loss = actor_loss.item()
|
|
|
|
# value loss
|
|
with torch.no_grad():
|
|
value_feat = imagined_rollout["sample"][:-1].detach()
|
|
value_targ = self.returns.detach()
|
|
|
|
value_dist = self.value_model(value_feat)
|
|
|
|
value_loss = -torch.mean(self.discounts * value_dist.log_prob(value_targ).unsqueeze(-1)) + past_value_loss
|
|
past_value_loss = value_loss.item()
|
|
|
|
# update target value
|
|
if step % self.args.value_target_update_freq == 0:
|
|
self.target_value_model = copy.deepcopy(self.value_model)
|
|
|
|
# counter for reward
|
|
#count = np.arange((counter-1) * (self.args.batch_size), (counter) * (self.args.batch_size))
|
|
count = (counter-1) * (self.args.batch_size)
|
|
if step % self.args.logging_freq:
|
|
writer.add_scalar('World Loss/World Loss', world_model_loss.detach().item(), self.data_buffer.steps)
|
|
writer.add_scalar('Main Models Loss/Encoder Loss', encoder_loss.detach().item(), self.data_buffer.steps)
|
|
writer.add_scalar('Main Models Loss/Decoder Loss', decoder_loss, self.data_buffer.steps)
|
|
writer.add_scalar('Actor Critic Loss/Actor Loss', actor_loss.detach().item(), self.data_buffer.steps)
|
|
writer.add_scalar('Actor Critic Loss/Value Loss', value_loss.detach().item(), self.data_buffer.steps)
|
|
writer.add_scalar('Actor Critic Loss/Reward Loss', reward_loss.detach().item(), self.data_buffer.steps)
|
|
writer.add_scalar('Bound Loss/Upper Bound Loss', ub_loss.detach().item(), self.data_buffer.steps)
|
|
writer.add_scalar('Bound Loss/Lower Bound Loss', lb_loss.detach().item(), self.data_buffer.steps)
|
|
step += 1
|
|
|
|
print(world_model_loss, actor_loss, value_loss)
|
|
|
|
# update actor model
|
|
self.actor_opt.zero_grad()
|
|
actor_loss.backward()
|
|
nn.utils.clip_grad_norm_(self.actor_model.parameters(), self.args.grad_clip_norm)
|
|
self.actor_opt.step()
|
|
|
|
# update world model
|
|
self.world_model_opt.zero_grad()
|
|
world_model_loss.backward()
|
|
nn.utils.clip_grad_norm_(self.world_model_parameters, self.args.grad_clip_norm)
|
|
self.world_model_opt.step()
|
|
|
|
# update value model
|
|
self.value_opt.zero_grad()
|
|
value_loss.backward()
|
|
nn.utils.clip_grad_norm_(self.value_model.parameters(), self.args.grad_clip_norm)
|
|
self.value_opt.step()
|
|
|
|
# update momentum encoder and projection head
|
|
soft_update_params(self.obs_encoder, self.obs_encoder_momentum, self.args.encoder_tau)
|
|
soft_update_params(self.prjoection_head, self.prjoection_head_momentum, self.args.encoder_tau)
|
|
|
|
rew_len = np.arange(count, count+self.args.episode_collection) if count != 0 else np.arange(0, self.args.batch_size)
|
|
for j in range(len(all_rews)):
|
|
writer.add_scalar('Rewards/Rewards', all_rews[j], rew_len[j])
|
|
|
|
print(step)
|
|
if step % 2850 == 0 and self.data_buffer.steps!=0: #self.args.evaluation_interval == 0:
|
|
print("Saving model")
|
|
path = os.path.dirname(os.path.realpath(__file__)) + "/saved_models/models.pth"
|
|
self.save_models(path)
|
|
self.evaluate()
|
|
|
|
|
|
def evaluate(self, eval_episodes=10):
|
|
path = path = os.path.dirname(os.path.realpath(__file__)) + "/saved_models/models.pth"
|
|
self.restore_checkpoint(path)
|
|
|
|
obs = self.env.reset()
|
|
done = False
|
|
|
|
#video = VideoRecorder(self.video_dir, resource_files=self.args.resource_files)
|
|
if self.args.save_video:
|
|
self.env.video.init(enabled=True)
|
|
episodic_rewards = []
|
|
for episode in range(eval_episodes):
|
|
rewards = 0
|
|
done = False
|
|
while not done:
|
|
with torch.no_grad():
|
|
obs = torch.tensor(obs.copy(), dtype=torch.float32).unsqueeze(0)
|
|
obs_processed = preprocess_obs(obs).to(device)
|
|
state = self.obs_encoder(obs_processed)["distribution"].sample()
|
|
action = self.actor_model(state).cpu().detach().numpy().squeeze()
|
|
|
|
next_obs, rew, done, _ = self.env.step(action)
|
|
rewards += rew
|
|
|
|
if self.args.save_video:
|
|
self.env.video.record(self.env)
|
|
self.env.video.save('/home/vedant/Curiosity/Curiosity/DPI/log/video/learned_model.mp4')
|
|
obs = next_obs
|
|
obs = self.env.reset()
|
|
episodic_rewards.append(rewards)
|
|
print("Episodic rewards: ", episodic_rewards)
|
|
print("Average episodic reward: ", np.mean(episodic_rewards))
|
|
|
|
def init_weights(self, m):
|
|
if isinstance(m, nn.Linear):
|
|
torch.nn.init.xavier_uniform_(m.weight)
|
|
m.bias.data.fill_(0.01)
|
|
|
|
|
|
def grouped_arrays(self,array):
|
|
indices = [0] + self.terms.tolist()
|
|
|
|
def subarrays():
|
|
for start, end in zip(indices[:-1], indices[1:]):
|
|
yield array[start:end]
|
|
|
|
try:
|
|
subarrays = np.stack(list(subarrays()), axis=0)
|
|
except ValueError:
|
|
subarrays = np.asarray(list(subarrays()))
|
|
|
|
return subarrays
|
|
|
|
def select_first_k(self, array, init_index, episode_number):
|
|
term_index = init_index + self.args.episode_length
|
|
array = array[episode_number]
|
|
|
|
array_list = []
|
|
for i in range(array.shape[0]):
|
|
array_list.append(array[i][init_index[i]:term_index[i]])
|
|
array = np.asarray(array_list)
|
|
|
|
if array.ndim == 5:
|
|
transposed_array = np.transpose(array, (1, 0, 2, 3, 4))
|
|
elif array.ndim == 4:
|
|
transposed_array = np.transpose(array, (1, 0, 2, 3))
|
|
elif array.ndim == 3:
|
|
transposed_array = np.transpose(array, (1, 0, 2))
|
|
elif array.ndim == 2:
|
|
transposed_array = np.transpose(array, (1, 0))
|
|
else:
|
|
transposed_array = np.expand_dims(array, axis=0)
|
|
return torch.tensor(transposed_array).float()
|
|
|
|
def _upper_bound_minimization(self, last_states, current_states, negative_current_states, predicted_current_states):
|
|
club_loss = self.club_sample(current_states, predicted_current_states, negative_current_states)
|
|
likelihood_loss = 0
|
|
return likelihood_loss, club_loss
|
|
|
|
def _past_encoder_loss(self, curr_states_dict, predicted_curr_states_dict):
|
|
# current state distribution
|
|
curr_states_dist = curr_states_dict["distribution"]
|
|
|
|
# predicted current state distribution
|
|
predicted_curr_states_dist = predicted_curr_states_dict["distribution"]
|
|
|
|
# KL divergence loss
|
|
loss = torch.mean(torch.distributions.kl.kl_divergence(curr_states_dist,predicted_curr_states_dist))
|
|
|
|
return loss
|
|
|
|
def get_features(self, x, momentum=False):
|
|
if self.args.aug:
|
|
crop_transform = T.RandomCrop(size=80)
|
|
cropped_x = torch.stack([crop_transform(x[i]) for i in range(x.size(0))])
|
|
padding = (2, 2, 2, 2)
|
|
x = F.pad(cropped_x, padding)
|
|
|
|
with torch.no_grad():
|
|
if momentum:
|
|
x = self.obs_encoder_momentum(x)
|
|
else:
|
|
x = self.obs_encoder(x)
|
|
return x
|
|
|
|
def _compute_lambda_return(self, rewards, values, discounts, td_lam, last_value):
|
|
next_values = torch.cat([values[1:], last_value.unsqueeze(0)],0)
|
|
targets = rewards + discounts * next_values * (1-td_lam)
|
|
rets =[]
|
|
last_rew = last_value
|
|
|
|
for t in range(rewards.shape[0]-1, -1, -1):
|
|
last_rew = targets[t] + discounts[t] * td_lam *(last_rew)
|
|
rets.append(last_rew)
|
|
|
|
returns = torch.flip(torch.stack(rets), [0])
|
|
return returns
|
|
|
|
def save_models(self, save_path):
|
|
torch.save(
|
|
{'rssm' : self.transition_model.state_dict(),
|
|
'actor': self.actor_model.state_dict(),
|
|
'reward_model': self.reward_model.state_dict(),
|
|
'obs_encoder': self.obs_encoder.state_dict(),
|
|
'obs_decoder': self.obs_decoder.state_dict(),
|
|
'actor_optimizer': self.actor_opt.state_dict(),
|
|
'value_optimizer': self.value_opt.state_dict(),
|
|
'world_model_optimizer': self.world_model_opt.state_dict(),}, save_path)
|
|
|
|
def restore_checkpoint(self, ckpt_path):
|
|
checkpoint = torch.load(ckpt_path)
|
|
self.transition_model.load_state_dict(checkpoint['rssm'])
|
|
self.actor_model.load_state_dict(checkpoint['actor'])
|
|
self.reward_model.load_state_dict(checkpoint['reward_model'])
|
|
self.obs_encoder.load_state_dict(checkpoint['obs_encoder'])
|
|
self.obs_decoder.load_state_dict(checkpoint['obs_decoder'])
|
|
self.world_model_opt.load_state_dict(checkpoint['world_model_optimizer'])
|
|
self.actor_opt.load_state_dict(checkpoint['actor_optimizer'])
|
|
self.value_opt.load_state_dict(checkpoint['value_optimizer'])
|
|
|
|
if __name__ == '__main__':
|
|
args = parse_args()
|
|
|
|
writer = SummaryWriter()
|
|
|
|
|
|
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
|
|
|
|
step = 0
|
|
total_steps = 500000
|
|
dpi = DPI(args)
|
|
dpi.train(step,total_steps)
|
|
dpi.evaluate() |