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agent.py
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agent.py
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# -*- coding: utf-8 -*-
# MIT License
#
# Copyright (c) 2017 Kai Arulkumaran
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
# ==============================================================================
from __future__ import division
import os
import numpy as np
import torch
from torch import optim
from torch.nn.utils import clip_grad_norm_
import kornia.augmentation as aug
import torch.nn as nn
from model import DQN
random_shift = nn.Sequential(aug.RandomCrop((80, 80)), nn.ReplicationPad2d(4), aug.RandomCrop((84, 84)))
aug = random_shift
class Agent():
def __init__(self, args, env):
self.args = args
self.action_space = env.action_space()
self.atoms = args.atoms
self.Vmin = args.V_min
self.Vmax = args.V_max
self.support = torch.linspace(args.V_min, args.V_max, self.atoms).to(device=args.device) # Support (range) of z
self.delta_z = (args.V_max - args.V_min) / (self.atoms - 1)
self.batch_size = args.batch_size
self.n = args.multi_step
self.discount = args.discount
self.norm_clip = args.norm_clip
self.coeff = 0.01 if args.game in ['pong', 'boxing', 'private_eye', 'freeway'] else 1.
self.online_net = DQN(args, self.action_space).to(device=args.device)
self.momentum_net = DQN(args, self.action_space).to(device=args.device)
if args.model: # Load pretrained model if provided
if os.path.isfile(args.model):
state_dict = torch.load(args.model, map_location='cpu') # Always load tensors onto CPU by default, will shift to GPU if necessary
if 'conv1.weight' in state_dict.keys():
for old_key, new_key in (('conv1.weight', 'convs.0.weight'), ('conv1.bias', 'convs.0.bias'), ('conv2.weight', 'convs.2.weight'), ('conv2.bias', 'convs.2.bias'), ('conv3.weight', 'convs.4.weight'), ('conv3.bias', 'convs.4.bias')):
state_dict[new_key] = state_dict[old_key] # Re-map state dict for old pretrained models
del state_dict[old_key] # Delete old keys for strict load_state_dict
self.online_net.load_state_dict(state_dict)
print("Loading pretrained model: " + args.model)
else: # Raise error if incorrect model path provided
raise FileNotFoundError(args.model)
self.online_net.train()
self.initialize_momentum_net()
self.momentum_net.train()
self.target_net = DQN(args, self.action_space).to(device=args.device)
self.update_target_net()
self.target_net.train()
for param in self.target_net.parameters():
param.requires_grad = False
for param in self.momentum_net.parameters():
param.requires_grad = False
self.optimiser = optim.Adam(self.online_net.parameters(), lr=args.learning_rate, eps=args.adam_eps)
# Resets noisy weights in all linear layers (of online net only)
def reset_noise(self):
self.online_net.reset_noise()
# Acts based on single state (no batch)
def act(self, state):
with torch.no_grad():
a, _ = self.online_net(state.unsqueeze(0))
return (a * self.support).sum(2).argmax(1).item()
# Acts with an ε-greedy policy (used for evaluation only)
def act_e_greedy(self, state, epsilon=0.001): # High ε can reduce evaluation scores drastically
return np.random.randint(0, self.action_space) if np.random.random() < epsilon else self.act(state)
def learn(self, mem):
# Sample transitions
idxs, states, actions, returns, next_states, nonterminals, weights = mem.sample(self.batch_size)
aug_states_1 = aug(states).to(device=self.args.device)
aug_states_2 = aug(states).to(device=self.args.device)
# Calculate current state probabilities (online network noise already sampled)
log_ps, _ = self.online_net(states, log=True) # Log probabilities log p(s_t, ·; θonline)
_, z_anch = self.online_net(aug_states_1, log=True)
_, z_target = self.momentum_net(aug_states_2, log=True)
z_proj = torch.matmul(self.online_net.W, z_target.T)
logits = torch.matmul(z_anch, z_proj)
logits = (logits - torch.max(logits, 1)[0][:, None])
logits = logits * 0.1
labels = torch.arange(logits.shape[0]).long().to(device=self.args.device)
moco_loss = (nn.CrossEntropyLoss()(logits, labels)).to(device=self.args.device)
log_ps_a = log_ps[range(self.batch_size), actions] # log p(s_t, a_t; θonline)
with torch.no_grad():
# Calculate nth next state probabilities
pns, _ = self.online_net(next_states) # Probabilities p(s_t+n, ·; θonline)
dns = self.support.expand_as(pns) * pns # Distribution d_t+n = (z, p(s_t+n, ·; θonline))
argmax_indices_ns = dns.sum(2).argmax(1) # Perform argmax action selection using online network: argmax_a[(z, p(s_t+n, a; θonline))]
self.target_net.reset_noise() # Sample new target net noise
pns, _ = self.target_net(next_states) # Probabilities p(s_t+n, ·; θtarget)
pns_a = pns[range(self.batch_size), argmax_indices_ns] # Double-Q probabilities p(s_t+n, argmax_a[(z, p(s_t+n, a; θonline))]; θtarget)
# Compute Tz (Bellman operator T applied to z)
Tz = returns.unsqueeze(1) + nonterminals * (self.discount ** self.n) * self.support.unsqueeze(0) # Tz = R^n + (γ^n)z (accounting for terminal states)
Tz = Tz.clamp(min=self.Vmin, max=self.Vmax) # Clamp between supported values
# Compute L2 projection of Tz onto fixed support z
b = (Tz - self.Vmin) / self.delta_z # b = (Tz - Vmin) / Δz
l, u = b.floor().to(torch.int64), b.ceil().to(torch.int64)
# Fix disappearing probability mass when l = b = u (b is int)
l[(u > 0) * (l == u)] -= 1
u[(l < (self.atoms - 1)) * (l == u)] += 1
# Distribute probability of Tz
m = states.new_zeros(self.batch_size, self.atoms)
offset = torch.linspace(0, ((self.batch_size - 1) * self.atoms), self.batch_size).unsqueeze(1).expand(self.batch_size, self.atoms).to(actions)
m.view(-1).index_add_(0, (l + offset).view(-1), (pns_a * (u.float() - b)).view(-1)) # m_l = m_l + p(s_t+n, a*)(u - b)
m.view(-1).index_add_(0, (u + offset).view(-1), (pns_a * (b - l.float())).view(-1)) # m_u = m_u + p(s_t+n, a*)(b - l)
loss = -torch.sum(m * log_ps_a, 1) # Cross-entropy loss (minimises DKL(m||p(s_t, a_t)))
loss = loss + (moco_loss * self.coeff)
self.online_net.zero_grad()
curl_loss = (weights * loss).mean()
curl_loss.mean().backward() # Backpropagate importance-weighted minibatch loss
clip_grad_norm_(self.online_net.parameters(), self.norm_clip) # Clip gradients by L2 norm
self.optimiser.step()
mem.update_priorities(idxs, loss.detach().cpu().numpy()) # Update priorities of sampled transitions
def update_target_net(self):
self.target_net.load_state_dict(self.online_net.state_dict())
def initialize_momentum_net(self):
for param_q, param_k in zip(self.online_net.parameters(), self.momentum_net.parameters()):
param_k.data.copy_(param_q.data) # update
param_k.requires_grad = False # not update by gradient
# Code for this function from https://github.com/facebookresearch/moco
@torch.no_grad()
def update_momentum_net(self, momentum=0.999):
for param_q, param_k in zip(self.online_net.parameters(), self.momentum_net.parameters()):
param_k.data.copy_(momentum * param_k.data + (1.- momentum) * param_q.data) # update
# Save model parameters on current device (don't move model between devices)
def save(self, path, name='model.pth'):
torch.save(self.online_net.state_dict(), os.path.join(path, name))
# Evaluates Q-value based on single state (no batch)
def evaluate_q(self, state):
with torch.no_grad():
a, _ = self.online_net(state.unsqueeze(0))
return (a * self.support).sum(2).max(1)[0].item()
def train(self):
self.online_net.train()
def eval(self):
self.online_net.eval()