Completing initial model and treating memory leak
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DPI/train.py
424
DPI/train.py
@ -1,15 +1,12 @@
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import numpy as np
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import torch
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import argparse
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import os
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import gym
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import time
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import json
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import dmc2gym
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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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@ -17,13 +14,12 @@ 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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@ -38,8 +34,9 @@ def parse_args():
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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=1, 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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@ -52,11 +49,11 @@ def parse_args():
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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=10000, type=int)
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parser.add_argument('--batch_size', default=20, type=int) #512
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parser.add_argument('--batch_size', default=30, type=int) #512
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parser.add_argument('--state_size', default=256, type=int)
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parser.add_argument('--hidden_size', default=128, type=int)
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parser.add_argument('--history_size', default=128, type=int)
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parser.add_argument('--num-units', type=int, default=200, help='num hidden units for reward/value/discount models')
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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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@ -64,15 +61,13 @@ def parse_args():
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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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# value
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parser.add_argument('--value_lr', default=1e-4, type=float)
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parser.add_argument('--value_lr', default=8e-5, 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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# reward
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parser.add_argument('--reward_lr', default=1e-4, type=float)
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# actor
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parser.add_argument('--actor_lr', default=1e-4, type=float)
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parser.add_argument('--actor_lr', default=8e-5, 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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@ -80,7 +75,7 @@ def parse_args():
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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('--encoder_feature_dim', default=50, type=int)
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parser.add_argument('--world_model_lr', default=1e-3, type=float)
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parser.add_argument('--world_model_lr', default=6e-4, type=float)
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parser.add_argument('--past_transition_lr', default=1e-3, type=float)
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parser.add_argument('--encoder_lr', default=1e-3, type=float)
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parser.add_argument('--encoder_tau', default=0.001, type=float)
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@ -100,6 +95,7 @@ def parse_args():
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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=1000, 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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@ -107,8 +103,6 @@ def parse_args():
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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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parser.add_argument('--port', default=2000, type=int)
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parser.add_argument('--num_likelihood_updates', default=5, type=int)
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args = parser.parse_args()
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return args
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@ -119,7 +113,7 @@ def parse_args():
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class DPI:
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def __init__(self, args, writer):
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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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@ -141,6 +135,8 @@ class DPI:
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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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@ -164,64 +160,64 @@ class DPI:
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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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)
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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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)
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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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)
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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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)
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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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)
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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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)
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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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)
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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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)
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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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)
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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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)
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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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)
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).to(device)
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# model parameters
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@ -237,7 +233,7 @@ class DPI:
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self.past_transition_opt = torch.optim.Adam(self.past_transition_parameters, self.args.past_transition_lr)
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# Create Modules
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self.world_model_modules = [self.obs_encoder, self.obs_decoder, self.value_model, self.transition_model, self.prjoection_head]
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self.world_model_modules = [self.obs_encoder, self.obs_decoder, self.reward_model, self.transition_model, self.prjoection_head]
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self.value_modules = [self.value_model]
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self.actor_modules = [self.actor_model]
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@ -249,21 +245,27 @@ class DPI:
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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):
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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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action = self.env.action_space.sample()
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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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@ -274,184 +276,222 @@ class DPI:
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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):
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# collect experience
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self.collect_sequences(self.args.batch_size)
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# Group observations and next_observations by steps from past to present
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last_observations = torch.tensor(self.data_buffer.group_steps(self.data_buffer,"observations")).float()[:self.args.episode_length-1]
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current_observations = torch.Tensor(self.data_buffer.group_steps(self.data_buffer,"next_observations")).float()[:self.args.episode_length-1]
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next_observations = torch.Tensor(self.data_buffer.group_steps(self.data_buffer,"next_observations")).float()[1:]
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actions = torch.Tensor(self.data_buffer.group_steps(self.data_buffer,"actions",obs=False)).float()[:self.args.episode_length-1]
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next_actions = torch.Tensor(self.data_buffer.group_steps(self.data_buffer,"actions",obs=False)).float()[1:]
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rewards = torch.Tensor(self.data_buffer.group_steps(self.data_buffer,"rewards",obs=False)).float()[1:]
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# Preprocessing
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last_observations = preprocess_obs(last_observations)
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current_observations = preprocess_obs(current_observations)
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next_observations = preprocess_obs(next_observations)
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# Initialize transition model states
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self.transition_model.init_states(self.args.batch_size, device="cpu") # (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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step = 0
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total_steps = 10000
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metrics = {}
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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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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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# 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.batch_size, random=True)
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all_rews = self.collect_sequences(self.args.batch_size, 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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# 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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# 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 = np.arange(self.args.batch_size * ((step//self.args.collection_interval)),self.args.batch_size * ((step//self.args.collection_interval)+1))
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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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# 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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#likeli_loss = torch.tensor(likeli_loss.numpy(),dtype=torch.float32, requires_grad=True)
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#ikeli_loss = likeli_loss.mean()
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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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# Calculate encoder loss
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encoder_loss = self._past_encoder_loss(self.current_states_dict,
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predicted_current_state_dict)
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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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#total_ub_loss += ub_loss
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#total_encoder_loss += encoder_loss
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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(self.args.collection_interval // self.args.episode_length+1):
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counter += 1
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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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# 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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# 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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# contrastive loss
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logits = self.contrastive_head(z_anchor, z_positive)
|
||||
labels = labels = torch.arange(logits.shape[0]).long()
|
||||
lb_loss = F.cross_entropy(logits, labels)
|
||||
# Predict current state from past state with transition model
|
||||
last_states_sample = self.last_states_dict["sample"]
|
||||
predicted_current_state_dict = self.transition_model.imagine_step(last_states_sample, self.action, self.history)
|
||||
self.history = predicted_current_state_dict["history"]
|
||||
|
||||
# behaviour learning
|
||||
with FreezeParameters(self.world_model_modules):
|
||||
imagine_horizon = self.args.imagine_horizon #np.minimum(self.args.imagine_horizon, self.args.episode_length-1-i)
|
||||
imagined_rollout = self.transition_model.imagine_rollout(self.current_states_dict["sample"].detach(),
|
||||
self.next_action, self.history.detach(),
|
||||
imagine_horizon)
|
||||
# Calculate upper bound loss
|
||||
likeli_loss, ub_loss = self._upper_bound_minimization(self.last_states_dict,
|
||||
self.current_states_dict,
|
||||
self.negative_current_states_dict,
|
||||
predicted_current_state_dict
|
||||
)
|
||||
|
||||
# decoder loss
|
||||
horizon = np.minimum(50-i, imagine_horizon)
|
||||
obs_dist = self.obs_decoder(imagined_rollout["sample"][:horizon])
|
||||
decoder_loss = -torch.mean(obs_dist.log_prob(next_observations[i:i+horizon][:,:,:3,:,:]))
|
||||
# Calculate encoder loss
|
||||
encoder_loss = self._past_encoder_loss(self.current_states_dict,
|
||||
predicted_current_state_dict)
|
||||
|
||||
# reward loss
|
||||
reward_dist = self.reward_model(self.current_states_dict["sample"])
|
||||
reward_loss = -torch.mean(reward_dist.log_prob(rewards[:-1]))
|
||||
# contrastive projection
|
||||
vec_anchor = predicted_current_state_dict["sample"]
|
||||
vec_positive = self.next_states_dict["sample"].detach()
|
||||
z_anchor = self.prjoection_head(vec_anchor, self.action)
|
||||
z_positive = self.prjoection_head_momentum(vec_positive, next_actions[i]).detach()
|
||||
|
||||
# update models
|
||||
world_model_loss = encoder_loss + ub_loss + lb_loss + decoder_loss * 1e-2
|
||||
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()
|
||||
# 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)
|
||||
|
||||
# actor loss
|
||||
with FreezeParameters(self.world_model_modules + self.value_modules):
|
||||
imag_rew_dist = self.reward_model(imagined_rollout["sample"])
|
||||
target_imag_val_dist = self.target_value_model(imagined_rollout["sample"])
|
||||
# behaviour learning
|
||||
with FreezeParameters(self.world_model_modules):
|
||||
imagine_horizon = self.args.imagine_horizon #np.minimum(self.args.imagine_horizon, self.args.episode_length-1-i)
|
||||
imagined_rollout = self.transition_model.imagine_rollout(self.current_states_dict["sample"].detach(),
|
||||
self.next_action, self.history.detach(),
|
||||
imagine_horizon)
|
||||
|
||||
imag_rews = imag_rew_dist.mean
|
||||
target_imag_vals = target_imag_val_dist.mean
|
||||
# decoder loss
|
||||
horizon = np.minimum(self.args.imagine_horizon, self.args.episode_length-1-i)
|
||||
obs_dist = self.obs_decoder(imagined_rollout["sample"][:horizon])
|
||||
decoder_loss = -torch.mean(obs_dist.log_prob(next_observations[i:i+horizon][:,:,:3,:,:]))
|
||||
|
||||
discounts = self.args.discount * torch.ones_like(imag_rews).detach()
|
||||
# reward loss
|
||||
reward_dist = self.reward_model(self.current_states_dict["sample"])
|
||||
reward_loss = -torch.mean(reward_dist.log_prob(rewards[:-1]))
|
||||
|
||||
self.target_returns = self._compute_lambda_return(imag_rews[:-1],
|
||||
target_imag_vals[:-1],
|
||||
discounts[:-1] ,
|
||||
self.args.td_lambda,
|
||||
target_imag_vals[-1])
|
||||
# update models
|
||||
world_model_loss = encoder_loss + 100 * ub_loss + lb_loss + reward_loss + decoder_loss * 1e-2
|
||||
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()
|
||||
|
||||
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.target_returns)
|
||||
# update momentum encoder
|
||||
soft_update_params(self.obs_encoder, self.obs_encoder_momentum, self.args.encoder_tau)
|
||||
|
||||
# update actor
|
||||
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 momentum projection head
|
||||
soft_update_params(self.prjoection_head, self.prjoection_head_momentum, self.args.encoder_tau)
|
||||
|
||||
# value loss
|
||||
with torch.no_grad():
|
||||
value_feat = imagined_rollout["sample"][:-1].detach()
|
||||
value_targ = self.target_returns.detach()
|
||||
# actor loss
|
||||
with FreezeParameters(self.world_model_modules + self.value_modules):
|
||||
imag_rew_dist = self.reward_model(imagined_rollout["sample"])
|
||||
target_imag_val_dist = self.target_value_model(imagined_rollout["sample"])
|
||||
|
||||
value_dist = self.value_model(value_feat)
|
||||
value_loss = -torch.mean(self.discounts * value_dist.log_prob(value_targ).unsqueeze(-1))
|
||||
imag_rews = imag_rew_dist.mean
|
||||
target_imag_vals = target_imag_val_dist.mean
|
||||
|
||||
# update value
|
||||
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()
|
||||
discounts = self.args.discount * torch.ones_like(imag_rews).detach()
|
||||
|
||||
# update target value
|
||||
if step % self.args.value_target_update_freq == 0:
|
||||
self.target_value_model = copy.deepcopy(self.value_model)
|
||||
self.target_returns = self._compute_lambda_return(imag_rews[:-1],
|
||||
target_imag_vals[:-1],
|
||||
discounts[:-1] ,
|
||||
self.args.td_lambda,
|
||||
target_imag_vals[-1])
|
||||
|
||||
# update momentum encoder
|
||||
soft_update_params(self.obs_encoder, self.obs_encoder_momentum, self.args.encoder_tau)
|
||||
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.target_returns)
|
||||
|
||||
# update momentum projection head
|
||||
soft_update_params(self.prjoection_head, self.prjoection_head_momentum, self.args.encoder_tau)
|
||||
# update actor
|
||||
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()
|
||||
|
||||
step += 1
|
||||
# value loss
|
||||
with torch.no_grad():
|
||||
value_feat = imagined_rollout["sample"][:-1].detach()
|
||||
value_targ = self.target_returns.detach()
|
||||
|
||||
if step % self.args.logging_freq:
|
||||
writer.add_scalar('Main Loss/World Loss', world_model_loss, step)
|
||||
writer.add_scalar('Main Models Loss/Encoder Loss', encoder_loss, step)
|
||||
writer.add_scalar('Main Models Loss/Decoder Loss', decoder_loss, step)
|
||||
writer.add_scalar('Actor Critic Loss/Actor Loss', actor_loss, step)
|
||||
writer.add_scalar('Actor Critic Loss/Value Loss', value_loss, step)
|
||||
writer.add_scalar('Actor Critic Loss/Reward Loss', reward_loss, step)
|
||||
writer.add_scalar('Bound Loss/Upper Bound Loss', ub_loss, step)
|
||||
writer.add_scalar('Bound Loss/Lower Bound Loss', lb_loss, step)
|
||||
value_dist = self.value_model(value_feat)
|
||||
value_loss = -torch.mean(self.discounts * value_dist.log_prob(value_targ).unsqueeze(-1))
|
||||
|
||||
"""
|
||||
if step % self.args.logging_freq:
|
||||
metrics['Upper Bound Loss'] = ub_loss.item()
|
||||
metrics['Encoder Loss'] = encoder_loss.item()
|
||||
metrics['Decoder Loss'] = decoder_loss.item()
|
||||
metrics["Lower Bound Loss"] = lb_loss.item()
|
||||
metrics["World Model Loss"] = world_model_loss.item()
|
||||
wandb.log(metrics)
|
||||
"""
|
||||
# update value
|
||||
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()
|
||||
|
||||
if step>total_steps:
|
||||
print("Training finished")
|
||||
break
|
||||
# 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))
|
||||
|
||||
|
||||
if step % self.args.logging_freq:
|
||||
writer.add_scalar('World Loss/World Loss', world_model_loss.detach().item(), step)
|
||||
writer.add_scalar('Main Models Loss/Encoder Loss', encoder_loss.detach().item(), step)
|
||||
writer.add_scalar('Main Models Loss/Decoder Loss', decoder_loss, step)
|
||||
writer.add_scalar('Actor Critic Loss/Actor Loss', actor_loss.detach().item(), step)
|
||||
writer.add_scalar('Actor Critic Loss/Value Loss', value_loss.detach().item(), step)
|
||||
writer.add_scalar('Actor Critic Loss/Reward Loss', reward_loss.detach().item(), step)
|
||||
writer.add_scalar('Bound Loss/Upper Bound Loss', ub_loss.detach().item(), step)
|
||||
writer.add_scalar('Bound Loss/Lower Bound Loss', lb_loss.detach().item(), step)
|
||||
|
||||
step += 1
|
||||
if step>total_steps:
|
||||
print("Training finished")
|
||||
break
|
||||
|
||||
# save model
|
||||
if step % self.args.saving_interval == 0:
|
||||
path = os.path.dirname(os.path.realpath(__file__)) + "/saved_models/models.pth"
|
||||
self.save_models(path)
|
||||
|
||||
#torch.cuda.empty_cache() # memory leak issues
|
||||
|
||||
for j in range(len(all_rews)):
|
||||
writer.add_scalar('Rewards/Rewards', all_rews[j], count[j])
|
||||
|
||||
|
||||
def evaluate(self, env, eval_episodes, render=False):
|
||||
|
||||
episode_rew = np.zeros((eval_episodes))
|
||||
|
||||
video_images = [[] for _ in range(eval_episodes)]
|
||||
|
||||
for i in range(eval_episodes):
|
||||
obs = env.reset()
|
||||
done = False
|
||||
prev_state = self.rssm.init_state(1, self.device)
|
||||
prev_action = torch.zeros(1, self.action_size).to(self.device)
|
||||
|
||||
while not done:
|
||||
with torch.no_grad():
|
||||
posterior, action = self.act_with_world_model(obs, prev_state, prev_action)
|
||||
action = action[0].cpu().numpy()
|
||||
next_obs, rew, done, _ = env.step(action)
|
||||
prev_state = posterior
|
||||
prev_action = torch.tensor(action, dtype=torch.float32).to(self.device).unsqueeze(0)
|
||||
|
||||
episode_rew[i] += rew
|
||||
|
||||
if render:
|
||||
video_images[i].append(obs['image'].transpose(1,2,0).copy())
|
||||
obs = next_obs
|
||||
return episode_rew, np.array(video_images[:self.args.max_videos_to_save])
|
||||
|
||||
def _upper_bound_minimization(self, last_states, current_states, negative_current_states, predicted_current_states):
|
||||
club_sample = CLUBSample(last_states,
|
||||
@ -469,8 +509,6 @@ class DPI:
|
||||
# predicted current state distribution
|
||||
predicted_curr_states_dist = predicted_curr_states_dict["distribution"]
|
||||
|
||||
|
||||
|
||||
# KL divergence loss
|
||||
loss = torch.distributions.kl.kl_divergence(curr_states_dist, predicted_curr_states_dist).mean()
|
||||
|
||||
@ -502,10 +540,26 @@ class DPI:
|
||||
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)
|
||||
|
||||
if __name__ == '__main__':
|
||||
args = parse_args()
|
||||
|
||||
writer = SummaryWriter()
|
||||
|
||||
dpi = DPI(args, writer)
|
||||
dpi.train()
|
||||
|
||||
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
|
||||
|
||||
step = 0
|
||||
total_steps = 10000
|
||||
dpi = DPI(args)
|
||||
dpi.train(step,total_steps)
|
||||
Loading…
Reference in New Issue
Block a user