Adding target value function updates and momentum updates
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9085abe684
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DPI/train.py
120
DPI/train.py
@ -7,10 +7,11 @@ import time
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import json
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import dmc2gym
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import copy
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import tqdm
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import wandb
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import utils
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from utils import ReplayBuffer, FreezeParameters, make_env, soft_update_params, save_image
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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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@ -19,6 +20,8 @@ from dmc2gym.wrappers import set_global_var
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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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@ -64,7 +67,7 @@ def parse_args():
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parser.add_argument('--value_lr', default=1e-4, 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=2, type=int)
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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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@ -80,7 +83,7 @@ def parse_args():
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parser.add_argument('--world_model_lr', default=1e-3, 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=0.005, type=float)
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parser.add_argument('--encoder_tau', default=0.001, type=float)
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parser.add_argument('--encoder_stride', default=1, type=int)
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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('--decoder_lr', default=1e-3, type=float)
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@ -116,7 +119,7 @@ def parse_args():
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class DPI:
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def __init__(self, args):
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def __init__(self, args, writer):
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# wandb config
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#run = wandb.init(project="dpi")
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@ -134,13 +137,10 @@ class DPI:
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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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self.env_clean = make_env(self.args)
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self.env_clean.seed(self.args.seed)
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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_clean = utils.FrameStack(self.env_clean, k=self.args.frame_stack)
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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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@ -162,18 +162,18 @@ class DPI:
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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), # (12,84,84)
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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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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), # (12,84,84)
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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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self.obs_decoder = ObservationDecoder(
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state_size=self.args.state_size, # 128
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output_shape=(self.args.frame_stack*self.args.channels,self.args.image_size,self.args.image_size) # (12,84,84)
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output_shape=(self.args.frame_stack*self.args.channels,self.args.image_size,self.args.image_size) # (9,84,84)
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)
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self.transition_model = TransitionModel(
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@ -251,41 +251,31 @@ class DPI:
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def collect_sequences(self, episodes):
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obs = self.env.reset()
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self.ob_mean = np.mean(obs, 0).astype(np.float32)
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self.ob_std = np.std(obs, 0).mean().astype(np.float32)
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#obs_clean = self.env_clean.reset()
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done = False
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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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#self.env_clean.video.init(enabled=True)
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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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next_obs, rew, done, _ = self.env.step(action)
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#next_obs_clean, _, done, _ = self.env_clean.step(action)
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self.data_buffer.add(obs, action, next_obs, rew, episode_count+1, done)
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#self.data_buffer_clean.add(obs_clean, action, next_obs_clean, 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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#self.env_clean.video.record(self.env_clean)
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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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#obs_clean = self.env_clean.reset()
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done=False
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else:
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obs = next_obs
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#obs_clean = next_obs_clean
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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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#self.env_clean.video.save('clean/%d.mp4' % episode_count)
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print("Collected {} random episodes".format(episode_count+1))
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def train(self):
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@ -300,6 +290,11 @@ class DPI:
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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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@ -357,41 +352,28 @@ class DPI:
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labels = labels = torch.arange(logits.shape[0]).long()
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lb_loss = F.cross_entropy(logits, labels)
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# update models
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"""
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print(likeli_loss)
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for i in range(self.args.num_likelihood_updates):
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self.past_transition_opt.zero_grad()
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print(likeli_loss)
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likeli_loss.backward()
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nn.utils.clip_grad_norm_(self.past_transition_parameters, self.args.grad_clip_norm)
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self.past_transition_opt.step()
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print(encoder_loss, ub_loss, lb_loss, step)
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"""
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world_model_loss = encoder_loss + ub_loss + lb_loss
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self.world_model_opt.zero_grad()
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world_model_loss.backward()
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nn.utils.clip_grad_norm_(self.world_model_parameters, self.args.grad_clip_norm)
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self.world_model_opt.step()
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"""
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if step % self.args.logging_freq:
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metrics['Upper Bound Loss'] = ub_loss.item()
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metrics['Encoder Loss'] = encoder_loss.item()
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metrics["Lower Bound Loss"] = lb_loss.item()
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metrics["World Model Loss"] = world_model_loss.item()
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wandb.log(metrics)
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"""
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# behaviour learning
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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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imagined_rollout = self.transition_model.imagine_rollout(self.current_states_dict["sample"].detach(),
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self.next_action, self.history.detach(),
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imagine_horizon)
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#print(imagined_rollout["sample"].shape, imagined_rollout["distribution"][0].sample().shape)
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# decoder loss
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horizon = np.minimum(50-i, imagine_horizon)
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obs_dist = self.obs_decoder(imagined_rollout["sample"][:horizon])
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decoder_loss = -torch.mean(obs_dist.log_prob(next_observations[i:i+horizon]))
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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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# update models
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world_model_loss = encoder_loss + ub_loss + lb_loss + decoder_loss * 1e-2
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self.world_model_opt.zero_grad()
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world_model_loss.backward()
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nn.utils.clip_grad_norm_(self.world_model_parameters, self.args.grad_clip_norm)
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self.world_model_opt.step()
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# actor loss
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with FreezeParameters(self.world_model_modules + self.value_modules):
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@ -413,6 +395,7 @@ class DPI:
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self.discounts = torch.cumprod(discounts, 0).detach()
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actor_loss = -torch.mean(self.discounts * self.target_returns)
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# update actor
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self.actor_opt.zero_grad()
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actor_loss.backward()
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nn.utils.clip_grad_norm_(self.actor_model.parameters(), self.args.grad_clip_norm)
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@ -426,17 +409,47 @@ class DPI:
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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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# update value
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self.value_opt.zero_grad()
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value_loss.backward()
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nn.utils.clip_grad_norm_(self.value_model.parameters(), self.args.grad_clip_norm)
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self.value_opt.step()
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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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# update momentum encoder
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soft_update_params(self.obs_encoder, self.obs_encoder_momentum, self.args.encoder_tau)
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# update momentum projection head
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soft_update_params(self.prjoection_head, self.prjoection_head_momentum, self.args.encoder_tau)
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step += 1
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if step % self.args.logging_freq:
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writer.add_scalar('Main Loss/World Loss', world_model_loss, step)
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writer.add_scalar('Main Models Loss/Encoder Loss', encoder_loss, step)
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writer.add_scalar('Main Models Loss/Decoder Loss', decoder_loss, step)
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writer.add_scalar('Actor Critic Loss/Actor Loss', actor_loss, step)
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writer.add_scalar('Actor Critic Loss/Value Loss', value_loss, step)
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writer.add_scalar('Actor Critic Loss/Reward Loss', reward_loss, step)
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writer.add_scalar('Bound Loss/Upper Bound Loss', ub_loss, step)
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writer.add_scalar('Bound Loss/Lower Bound Loss', lb_loss, step)
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"""
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if step % self.args.logging_freq:
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metrics['Upper Bound Loss'] = ub_loss.item()
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metrics['Encoder Loss'] = encoder_loss.item()
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metrics['Decoder Loss'] = decoder_loss.item()
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metrics["Lower Bound Loss"] = lb_loss.item()
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metrics["World Model Loss"] = world_model_loss.item()
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wandb.log(metrics)
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"""
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if step>total_steps:
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print("Training finished")
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break
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#print(total_ub_loss, total_encoder_loss)
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@ -464,9 +477,6 @@ class DPI:
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return loss
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def get_features(self, x, momentum=False):
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import torchvision.transforms.functional as fn
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x = x/255.0 - 0.5 # Preprocessing
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if self.args.aug:
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x = T.RandomCrop((80, 80))(x) # (None,80,80,4)
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x = T.functional.pad(x, (4, 4, 4, 4), "symmetric") # (None,88,88,4)
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@ -495,5 +505,7 @@ class DPI:
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if __name__ == '__main__':
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args = parse_args()
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dpi = DPI(args)
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writer = SummaryWriter()
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dpi = DPI(args, writer)
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dpi.train()
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