579 lines
30 KiB
Python
579 lines
30 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=51, 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=50, type=int) #512
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parser.add_argument('--state_size', default=512, 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=256, 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=15, 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=1e-3, 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=1e-3, 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-3, type=float)
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parser.add_argument('--encoder_tau', default=0.001, 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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# 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.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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# 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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# 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())
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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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# 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]
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self.value_modules = [self.value_model]
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self.actor_modules = [self.actor_model]
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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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#video = VideoRecorder(self.video_dir if args.save_video else None, resource_files=args.resource_files)
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for episode_count in tqdm.tqdm(range(episodes), desc='Collecting episodes'):
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#if args.save_video:
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# self.env.video.init(enabled=True)
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epi_reward = 0
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for i in range(self.args.episode_length):
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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 = torch.unsqueeze(torch.tensor(obs).float(),0).to(device)
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state = self.obs_encoder(obs_torch)["distribution"].sample()
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action = self.actor_model(state).cpu().detach().numpy().squeeze()
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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, episode_count+1, done)
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#if args.save_video:
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# self.env.video.record(self.env)
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if done: #or i == self.args.episode_length-1:
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obs = self.env.reset()
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done=False
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else:
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obs = next_obs
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epi_reward += rew
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all_rews.append(epi_reward)
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#if args.save_video:
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# self.env.video.save('noisy/%d.mp4' % episode_count)
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#print("Collected {} random episodes".format(episode_count+1))
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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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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(10, random=False, actor_model=actor, encoder_model=encoder)
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else:
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all_rews = self.collect_sequences(self.args.batch_size, random=True)
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# Group by steps and sample random batch
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#random_indices = self.data_buffer.sample_random_idx(self.args.batch_size * ((step//self.args.collection_interval)+1)) # random indices for batch
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#random_indices = self.data_buffer.sample_random_idx(self.data_buffer.steps//self.args.episode_length)
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final_idx = self.data_buffer.group_steps(self.data_buffer, "observations").shape[1]
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random_indices = self.data_buffer.sample_random_idx(final_idx, last=True)
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last_observations = self.data_buffer.group_and_sample_random_batch(self.data_buffer,"observations", "cpu", random_indices=random_indices)
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current_observations = self.data_buffer.group_and_sample_random_batch(self.data_buffer,"next_observations", device="cpu", random_indices=random_indices)
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next_observations = self.data_buffer.group_and_sample_random_batch(self.data_buffer,"next_observations", device="cpu", offset=1, random_indices=random_indices)
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actions = self.data_buffer.group_and_sample_random_batch(self.data_buffer,"actions", device=device, is_obs=False, random_indices=random_indices)
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next_actions = self.data_buffer.group_and_sample_random_batch(self.data_buffer,"actions", device=device, is_obs=False, offset=1, random_indices=random_indices)
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rewards = self.data_buffer.group_and_sample_random_batch(self.data_buffer,"rewards", device=device, is_obs=False, offset=1, random_indices=random_indices)
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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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# Train encoder
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if step == 0:
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step += 1
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for _ in range(1):#(self.args.collection_interval // self.args.episode_length+1):
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counter += 1
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past_encoder_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
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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, self.args.episode_length-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.obs_encoder(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,
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self.current_states_dict,
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self.negative_current_states_dict,
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predicted_current_state_dict
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)
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# Calculate encoder loss
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encoder_loss = past_encoder_loss + self._past_encoder_loss(self.current_states_dict, predicted_current_state_dict)
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past_encoder_loss = encoder_loss.item()
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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].view(-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][:,:,:3,:,:]))
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# contrastive projection
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vec_anchor = predicted_current_state_dict["sample"]
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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
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logits = self.contrastive_head(z_anchor, z_positive)
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labels = torch.arange(logits.shape[0]).long().to(device)
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lb_loss = F.cross_entropy(logits, labels)
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# reward loss
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reward_dist = self.reward_model(self.current_states_dict["sample"])
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reward_loss = -torch.mean(reward_dist.log_prob(rewards[:-1]))
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# world model loss
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world_model_loss = encoder_loss + ub_loss + lb_loss + reward_loss + decoder_loss
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# actor loss
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with FreezeParameters(self.world_model_modules):
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imagine_horizon = self.args.imagine_horizon #np.minimum(self.args.imagine_horizon, self.args.episode_length-1-i)
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action = self.actor_model(self.current_states_dict["sample"])
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imagined_rollout = self.transition_model.imagine_rollout(self.current_states_dict["sample"].detach(),
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action, self.history.detach(),
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imagine_horizon)
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with FreezeParameters(self.world_model_modules + self.value_modules):
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imag_rewards = self.reward_model(imagined_rollout["sample"]).mean
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imag_values = self.value_model(imagined_rollout["sample"]).mean
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discounts = self.args.discount * torch.ones_like(imag_rewards).detach()
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self.returns = self._compute_lambda_return(imag_rewards[:-1],
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imag_values[:-1],
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discounts[:-1] ,
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self.args.td_lambda,
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imag_values[-1])
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|
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discounts = torch.cat([torch.ones_like(discounts[:1]), discounts[1:-1]], 0)
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self.discounts = torch.cumprod(discounts, 0).detach()
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actor_loss = -torch.mean(self.discounts * self.returns)
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# value loss
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with torch.no_grad():
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value_feat = imagined_rollout["sample"][:-1].detach()
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value_targ = self.returns.detach()
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|
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value_dist = self.value_model(value_feat)
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value_loss = -torch.mean(self.discounts * value_dist.log_prob(value_targ).unsqueeze(-1))
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|
|
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# update models
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self.world_model_opt.zero_grad()
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self.actor_opt.zero_grad()
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self.value_opt.zero_grad()
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|
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world_model_loss.backward()
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actor_loss.backward()
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value_loss.backward()
|
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|
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nn.utils.clip_grad_norm_(self.world_model_parameters, self.args.grad_clip_norm)
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nn.utils.clip_grad_norm_(self.actor_model.parameters(), self.args.grad_clip_norm)
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nn.utils.clip_grad_norm_(self.value_model.parameters(), self.args.grad_clip_norm)
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self.world_model_opt.step()
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self.actor_opt.step()
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self.value_opt.step()
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|
|
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# update momentum encoder and projection head
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soft_update_params(self.obs_encoder, self.obs_encoder_momentum, self.args.encoder_tau)
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soft_update_params(self.prjoection_head, self.prjoection_head_momentum, self.args.encoder_tau)
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|
|
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# update target value
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#if step % self.args.value_target_update_freq == 0:
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# self.target_value_model = copy.deepcopy(self.value_model)
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|
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# counter for reward
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count = np.arange((counter-1) * (self.args.batch_size), (counter) * (self.args.batch_size))
|
|
|
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if step % self.args.logging_freq:
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writer.add_scalar('World Loss/World Loss', world_model_loss.detach().item(), self.data_buffer.steps)
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writer.add_scalar('Main Models Loss/Encoder Loss', encoder_loss.detach().item(), self.data_buffer.steps)
|
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writer.add_scalar('Main Models Loss/Decoder Loss', decoder_loss, self.data_buffer.steps)
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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)
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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)
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|
writer.add_scalar('Bound Loss/Lower Bound Loss', lb_loss.detach().item(), self.data_buffer.steps)
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|
|
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step += 1
|
|
|
|
|
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# save model
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|
#if step % 500 == 0:#self.args.saving_interval == 0:
|
|
# print("Saving model")
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|
# path = os.path.dirname(os.path.realpath(__file__)) + "/saved_models/models.pth"
|
|
# self.save_models(path)
|
|
|
|
for j in range(len(all_rews)):
|
|
writer.add_scalar('Rewards/Rewards', all_rews[j], count[j])
|
|
|
|
#print(self.data_buffer.steps , ((self.args.episode_length-1) * self.args.batch_size * 5))
|
|
if self.data_buffer.steps % 5100 == 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 = torch.unsqueeze(torch.tensor(obs).float(),0).to(device)
|
|
state = self.obs_encoder(obs_torch)["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 _upper_bound_minimization(self, last_states, current_states, negative_current_states, predicted_current_states):
|
|
club_sample = CLUBSample(last_states,
|
|
current_states,
|
|
negative_current_states,
|
|
predicted_current_states)
|
|
likelihood_loss = club_sample.learning_loss()
|
|
club_loss = club_sample()
|
|
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 = 200000
|
|
dpi = DPI(args)
|
|
dpi.train(step,total_steps)
|
|
dpi.evaluate() |