476 lines
18 KiB
Python
476 lines
18 KiB
Python
import copy
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import torch
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from torch import nn
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import numpy as np
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from PIL import ImageColor, Image, ImageDraw, ImageFont
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import networks
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import tools
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to_np = lambda x: x.detach().cpu().numpy()
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class RewardEMA(object):
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"""running mean and std"""
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def __init__(self, device, alpha=1e-2):
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self.device = device
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self.values = torch.zeros((2,)).to(device)
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self.alpha = alpha
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self.range = torch.tensor([0.05, 0.95]).to(device)
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def __call__(self, x):
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flat_x = torch.flatten(x.detach())
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x_quantile = torch.quantile(input=flat_x, q=self.range)
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self.values = self.alpha * x_quantile + (1 - self.alpha) * self.values
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scale = torch.clip(self.values[1] - self.values[0], min=1.0)
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offset = self.values[0]
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return offset.detach(), scale.detach()
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class WorldModel(nn.Module):
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def __init__(self, obs_space, act_space, step, config):
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super(WorldModel, self).__init__()
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self._step = step
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self._use_amp = True if config.precision == 16 else False
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self._config = config
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shapes = {k: tuple(v.shape) for k, v in obs_space.spaces.items()}
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self.encoder = networks.MultiEncoder(shapes, **config.encoder)
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self.embed_size = self.encoder.outdim
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self.dynamics = networks.RSSM(
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config.dyn_stoch,
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config.dyn_deter,
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config.dyn_hidden,
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config.dyn_input_layers,
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config.dyn_output_layers,
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config.dyn_rec_depth,
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config.dyn_shared,
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config.dyn_discrete,
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config.act,
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config.norm,
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config.dyn_mean_act,
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config.dyn_std_act,
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config.dyn_temp_post,
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config.dyn_min_std,
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config.dyn_cell,
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config.unimix_ratio,
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config.initial,
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config.num_actions,
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self.embed_size,
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config.device,
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)
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self.heads = nn.ModuleDict()
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if config.dyn_discrete:
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feat_size = config.dyn_stoch * config.dyn_discrete + config.dyn_deter
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else:
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feat_size = config.dyn_stoch + config.dyn_deter
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self.heads["decoder"] = networks.MultiDecoder(
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feat_size, shapes, **config.decoder
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)
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if config.reward_head == "symlog_disc":
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self.heads["reward"] = networks.MLP(
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feat_size, # pytorch version
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(255,),
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config.reward_layers,
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config.units,
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config.act,
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config.norm,
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dist=config.reward_head,
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outscale=0.0,
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device=config.device,
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)
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else:
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self.heads["reward"] = networks.MLP(
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feat_size, # pytorch version
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[],
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config.reward_layers,
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config.units,
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config.act,
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config.norm,
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dist=config.reward_head,
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outscale=0.0,
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device=config.device,
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)
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self.heads["cont"] = networks.MLP(
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feat_size, # pytorch version
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[],
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config.cont_layers,
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config.units,
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config.act,
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config.norm,
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dist="binary",
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device=config.device,
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)
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for name in config.grad_heads:
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assert name in self.heads, name
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self._model_opt = tools.Optimizer(
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"model",
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self.parameters(),
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config.model_lr,
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config.opt_eps,
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config.grad_clip,
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config.weight_decay,
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opt=config.opt,
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use_amp=self._use_amp,
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)
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self._scales = dict(reward=config.reward_scale, cont=config.cont_scale)
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def _train(self, data):
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# action (batch_size, batch_length, act_dim)
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# image (batch_size, batch_length, h, w, ch)
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# reward (batch_size, batch_length)
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# discount (batch_size, batch_length)
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data = self.preprocess(data)
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with tools.RequiresGrad(self):
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with torch.cuda.amp.autocast(self._use_amp):
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embed = self.encoder(data)
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post, prior = self.dynamics.observe(
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embed, data["action"], data["is_first"]
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)
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kl_free = tools.schedule(self._config.kl_free, self._step)
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dyn_scale = tools.schedule(self._config.dyn_scale, self._step)
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rep_scale = tools.schedule(self._config.rep_scale, self._step)
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kl_loss, kl_value, dyn_loss, rep_loss = self.dynamics.kl_loss(
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post, prior, kl_free, dyn_scale, rep_scale
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)
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preds = {}
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for name, head in self.heads.items():
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grad_head = name in self._config.grad_heads
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feat = self.dynamics.get_feat(post)
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feat = feat if grad_head else feat.detach()
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pred = head(feat)
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if type(pred) is dict:
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preds.update(pred)
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else:
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preds[name] = pred
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losses = {}
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for name, pred in preds.items():
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like = pred.log_prob(data[name])
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losses[name] = -torch.mean(like) * self._scales.get(name, 1.0)
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model_loss = sum(losses.values()) + kl_loss
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metrics = self._model_opt(model_loss, self.parameters())
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metrics.update({f"{name}_loss": to_np(loss) for name, loss in losses.items()})
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metrics["kl_free"] = kl_free
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metrics["dyn_scale"] = dyn_scale
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metrics["rep_scale"] = rep_scale
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metrics["dyn_loss"] = to_np(dyn_loss)
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metrics["rep_loss"] = to_np(rep_loss)
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metrics["kl"] = to_np(torch.mean(kl_value))
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with torch.cuda.amp.autocast(self._use_amp):
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metrics["prior_ent"] = to_np(
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torch.mean(self.dynamics.get_dist(prior).entropy())
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)
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metrics["post_ent"] = to_np(
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torch.mean(self.dynamics.get_dist(post).entropy())
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)
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context = dict(
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embed=embed,
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feat=self.dynamics.get_feat(post),
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kl=kl_value,
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postent=self.dynamics.get_dist(post).entropy(),
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)
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post = {k: v.detach() for k, v in post.items()}
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return post, context, metrics
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def preprocess(self, obs):
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obs = obs.copy()
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obs["image"] = torch.Tensor(obs["image"]) / 255.0 - 0.5
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# (batch_size, batch_length) -> (batch_size, batch_length, 1)
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obs["reward"] = torch.Tensor(obs["reward"]).unsqueeze(-1)
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if "discount" in obs:
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obs["discount"] *= self._config.discount
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# (batch_size, batch_length) -> (batch_size, batch_length, 1)
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obs["discount"] = torch.Tensor(obs["discount"]).unsqueeze(-1)
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if "is_terminal" in obs:
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# this label is necessary to train cont_head
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obs["cont"] = torch.Tensor(1.0 - obs["is_terminal"]).unsqueeze(-1)
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else:
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raise ValueError('"is_terminal" was not found in observation.')
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obs = {k: torch.Tensor(v).to(self._config.device) for k, v in obs.items()}
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return obs
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def video_pred(self, data):
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data = self.preprocess(data)
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embed = self.encoder(data)
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states, _ = self.dynamics.observe(
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embed[:6, :5], data["action"][:6, :5], data["is_first"][:6, :5]
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)
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recon = self.heads["decoder"](self.dynamics.get_feat(states))["image"].mode()[
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:6
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]
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reward_post = self.heads["reward"](self.dynamics.get_feat(states)).mode()[:6]
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init = {k: v[:, -1] for k, v in states.items()}
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prior = self.dynamics.imagine(data["action"][:6, 5:], init)
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openl = self.heads["decoder"](self.dynamics.get_feat(prior))["image"].mode()
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reward_prior = self.heads["reward"](self.dynamics.get_feat(prior)).mode()
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# observed image is given until 5 steps
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model = torch.cat([recon[:, :5], openl], 1)
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truth = data["image"][:6] + 0.5
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model = model + 0.5
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error = (model - truth + 1.0) / 2.0
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return torch.cat([truth, model, error], 2)
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class ImagBehavior(nn.Module):
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def __init__(self, config, world_model, stop_grad_actor=True, reward=None):
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super(ImagBehavior, self).__init__()
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self._use_amp = True if config.precision == 16 else False
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self._config = config
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self._world_model = world_model
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self._stop_grad_actor = stop_grad_actor
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self._reward = reward
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if config.dyn_discrete:
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feat_size = config.dyn_stoch * config.dyn_discrete + config.dyn_deter
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else:
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feat_size = config.dyn_stoch + config.dyn_deter
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self.actor = networks.ActionHead(
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feat_size,
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config.num_actions,
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config.actor_layers,
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config.units,
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config.act,
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config.norm,
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config.actor_dist,
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config.actor_init_std,
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config.actor_min_std,
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config.actor_max_std,
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config.actor_temp,
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outscale=1.0,
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unimix_ratio=config.action_unimix_ratio,
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)
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if config.value_head == "symlog_disc":
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self.value = networks.MLP(
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feat_size,
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(255,),
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config.value_layers,
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config.units,
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config.act,
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config.norm,
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config.value_head,
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outscale=0.0,
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device=config.device,
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)
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else:
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self.value = networks.MLP(
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feat_size,
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[],
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config.value_layers,
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config.units,
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config.act,
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config.norm,
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config.value_head,
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outscale=0.0,
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device=config.device,
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)
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if config.slow_value_target:
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self._slow_value = copy.deepcopy(self.value)
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self._updates = 0
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kw = dict(wd=config.weight_decay, opt=config.opt, use_amp=self._use_amp)
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self._actor_opt = tools.Optimizer(
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"actor",
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self.actor.parameters(),
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config.actor_lr,
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config.ac_opt_eps,
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config.actor_grad_clip,
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**kw,
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)
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self._value_opt = tools.Optimizer(
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"value",
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self.value.parameters(),
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config.value_lr,
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config.ac_opt_eps,
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config.value_grad_clip,
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**kw,
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)
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if self._config.reward_EMA:
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self.reward_ema = RewardEMA(device=self._config.device)
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def _train(
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self,
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start,
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objective=None,
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action=None,
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reward=None,
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imagine=None,
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tape=None,
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repeats=None,
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):
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objective = objective or self._reward
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self._update_slow_target()
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metrics = {}
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with tools.RequiresGrad(self.actor):
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with torch.cuda.amp.autocast(self._use_amp):
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imag_feat, imag_state, imag_action = self._imagine(
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start, self.actor, self._config.imag_horizon, repeats
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)
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reward = objective(imag_feat, imag_state, imag_action)
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actor_ent = self.actor(imag_feat).entropy()
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state_ent = self._world_model.dynamics.get_dist(imag_state).entropy()
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# this target is not scaled
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# slow is flag to indicate whether slow_target is used for lambda-return
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target, weights, base = self._compute_target(
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imag_feat, imag_state, imag_action, reward, actor_ent, state_ent
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)
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actor_loss, mets = self._compute_actor_loss(
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imag_feat,
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imag_state,
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imag_action,
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target,
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actor_ent,
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state_ent,
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weights,
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base,
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)
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metrics.update(mets)
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value_input = imag_feat
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with tools.RequiresGrad(self.value):
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with torch.cuda.amp.autocast(self._use_amp):
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value = self.value(value_input[:-1].detach())
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target = torch.stack(target, dim=1)
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# (time, batch, 1), (time, batch, 1) -> (time, batch)
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value_loss = -value.log_prob(target.detach())
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slow_target = self._slow_value(value_input[:-1].detach())
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if self._config.slow_value_target:
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value_loss = value_loss - value.log_prob(
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slow_target.mode().detach()
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)
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if self._config.value_decay:
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value_loss += self._config.value_decay * value.mode()
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# (time, batch, 1), (time, batch, 1) -> (1,)
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value_loss = torch.mean(weights[:-1] * value_loss[:, :, None])
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metrics.update(tools.tensorstats(value.mode(), "value"))
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metrics.update(tools.tensorstats(target, "target"))
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metrics.update(tools.tensorstats(reward, "imag_reward"))
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if self._config.actor_dist in ["onehot"]:
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metrics.update(
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tools.tensorstats(
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torch.argmax(imag_action, dim=-1).float(), "imag_action"
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)
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)
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else:
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metrics.update(tools.tensorstats(imag_action, "imag_action"))
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metrics["actor_entropy"] = to_np(torch.mean(actor_ent))
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with tools.RequiresGrad(self):
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metrics.update(self._actor_opt(actor_loss, self.actor.parameters()))
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metrics.update(self._value_opt(value_loss, self.value.parameters()))
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return imag_feat, imag_state, imag_action, weights, metrics
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def _imagine(self, start, policy, horizon, repeats=None):
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dynamics = self._world_model.dynamics
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if repeats:
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raise NotImplemented("repeats is not implemented in this version")
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flatten = lambda x: x.reshape([-1] + list(x.shape[2:]))
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start = {k: flatten(v) for k, v in start.items()}
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def step(prev, _):
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state, _, _ = prev
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feat = dynamics.get_feat(state)
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inp = feat.detach() if self._stop_grad_actor else feat
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action = policy(inp).sample()
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succ = dynamics.img_step(state, action, sample=self._config.imag_sample)
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return succ, feat, action
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succ, feats, actions = tools.static_scan(
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step, [torch.arange(horizon)], (start, None, None)
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)
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states = {k: torch.cat([start[k][None], v[:-1]], 0) for k, v in succ.items()}
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if repeats:
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raise NotImplemented("repeats is not implemented in this version")
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return feats, states, actions
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def _compute_target(
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self, imag_feat, imag_state, imag_action, reward, actor_ent, state_ent
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):
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if "cont" in self._world_model.heads:
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inp = self._world_model.dynamics.get_feat(imag_state)
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discount = self._config.discount * self._world_model.heads["cont"](inp).mean
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else:
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discount = self._config.discount * torch.ones_like(reward)
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if self._config.future_entropy and self._config.actor_entropy() > 0:
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reward += self._config.actor_entropy() * actor_ent
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if self._config.future_entropy and self._config.actor_state_entropy() > 0:
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reward += self._config.actor_state_entropy() * state_ent
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value = self.value(imag_feat).mode()
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target = tools.lambda_return(
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reward[1:],
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value[:-1],
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discount[1:],
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bootstrap=value[-1],
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lambda_=self._config.discount_lambda,
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axis=0,
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)
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weights = torch.cumprod(
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torch.cat([torch.ones_like(discount[:1]), discount[:-1]], 0), 0
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).detach()
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return target, weights, value[:-1]
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def _compute_actor_loss(
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self,
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imag_feat,
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imag_state,
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imag_action,
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target,
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actor_ent,
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state_ent,
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weights,
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base,
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):
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metrics = {}
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inp = imag_feat.detach() if self._stop_grad_actor else imag_feat
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policy = self.actor(inp)
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actor_ent = policy.entropy()
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# Q-val for actor is not transformed using symlog
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target = torch.stack(target, dim=1)
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if self._config.reward_EMA:
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offset, scale = self.reward_ema(target)
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normed_target = (target - offset) / scale
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normed_base = (base - offset) / scale
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adv = normed_target - normed_base
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metrics.update(tools.tensorstats(normed_target, "normed_target"))
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values = self.reward_ema.values
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metrics["EMA_005"] = to_np(values[0])
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metrics["EMA_095"] = to_np(values[1])
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if self._config.imag_gradient == "dynamics":
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actor_target = adv
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elif self._config.imag_gradient == "reinforce":
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actor_target = (
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policy.log_prob(imag_action)[:-1][:, :, None]
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* (target - self.value(imag_feat[:-1]).mode()).detach()
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)
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elif self._config.imag_gradient == "both":
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actor_target = (
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policy.log_prob(imag_action)[:-1][:, :, None]
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* (target - self.value(imag_feat[:-1]).mode()).detach()
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)
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mix = self._config.imag_gradient_mix()
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actor_target = mix * target + (1 - mix) * actor_target
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metrics["imag_gradient_mix"] = mix
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else:
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raise NotImplementedError(self._config.imag_gradient)
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if not self._config.future_entropy and (self._config.actor_entropy() > 0):
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actor_entropy = self._config.actor_entropy() * actor_ent[:-1][:, :, None]
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actor_target += actor_entropy
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if not self._config.future_entropy and (self._config.actor_state_entropy() > 0):
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state_entropy = self._config.actor_state_entropy() * state_ent[:-1]
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actor_target += state_entropy
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metrics["actor_state_entropy"] = to_np(torch.mean(state_entropy))
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actor_loss = -torch.mean(weights[:-1] * actor_target)
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return actor_loss, metrics
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def _update_slow_target(self):
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if self._config.slow_value_target:
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if self._updates % self._config.slow_target_update == 0:
|
|
mix = self._config.slow_target_fraction
|
|
for s, d in zip(self.value.parameters(), self._slow_value.parameters()):
|
|
d.data = mix * s.data + (1 - mix) * d.data
|
|
self._updates += 1
|