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agent.py
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agent.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
import numpy as np
import random
from collections import deque
import torch
import torch.nn.functional as F
import torch.optim as optim
from model import QNetwork
class Agent():
"""Implements a learning agent to solve the environment
Args:
state_size (int): dimension of each state
action_size (int): dimension of each action
train (bool, optional): whether the agent should be used in training mode
device (str, optional): device to host model
lin_features (int, optional): number of nodes in hidden layers
bn (bool, optional): whether batch norm should be added after hidden layer
dropout_prob (float, optional): dropout probability of hidden layers
buffer_size (int, optional): replay buffer size
batch_size (int, optional):
lr (float, optional): learning rate
gamma (float, optional): discount factor
tau (float, optional): for soft update of target parameters
update_freq (int, optional): number of steps between each update
"""
def __init__(self, state_size, action_size, train=False, device=None,
lin_feats=64, bn=False, dropout_prob=0., buffer_size=1e5,
batch_size=64, lr=5e-4, gamma=0.99, tau=1e-3, update_freq=4):
self.state_size = state_size
self.action_size = action_size
self.train = train
if device is None:
if torch.cuda.is_available():
device = 'cuda:0'
else:
device = 'cpu'
self.device = torch.device(device)
# Q-Network
self.qnetwork_local = QNetwork(state_size, action_size,
lin_feats, bn, dropout_prob).to(self.device)
# Training mode attributes
if self.train:
self.bs = batch_size
self.gamma = gamma
self.tau = tau
self.update_freq = update_freq
self.qnetwork_target = QNetwork(state_size, action_size,
lin_feats, bn, dropout_prob).to(self.device)
self.optimizer = optim.Adam(self.qnetwork_local.parameters(), lr=lr)
# Replay memory
self.memory = ReplayBuffer(action_size, buffer_size, self.bs, self.device)
# Initialize time step (for updating every UPDATE_EVERY steps)
self.t_step = 0
else:
self.qnetwork_local.eval()
def act(self, state, eps=0.):
"""Returns actions for given state as per current policy.
Args:
state (array_like): current state
eps (float): epsilon, for epsilon-greedy action selection
Returns:
int: selected action index
"""
state = torch.from_numpy(state).float().unsqueeze(0).to(self.device)
self.qnetwork_local.eval()
with torch.no_grad():
action_values = self.qnetwork_local(state)
if self.train:
self.qnetwork_local.train()
# Epsilon-greedy action selection
if not self.train or random.random() > eps:
return np.argmax(action_values.cpu().data.numpy())
else:
return random.choice(np.arange(self.action_size))
def step(self, state, action, reward, next_state, done):
"""Let the agent perform a training step
Args:
state (array_like): current state
action (int): action index
reward (float): received reward
next_state (array_like): next state
done (bool): whether the episode is over
"""
if not self.train:
raise ValueError('agent cannot be trained if constructor argument train=False')
# Save experience in replay memory
self.memory.add(state, action, reward, next_state, done)
# Learn every UPDATE_EVERY time steps.
self.t_step = (self.t_step + 1) % self.update_freq
if self.t_step == 0:
# If enough samples are available in memory, get random subset and learn
if len(self.memory) > self.bs:
experiences = self.memory.sample()
self.learn(experiences, self.gamma)
def learn(self, experiences, gamma):
"""Update value parameters using given batch of experience tuples.
Args:
experiences (Tuple[torch.Tensor]): tuple of (s, a, r, s', done) tuples
gamma (float): discount factor
"""
if not self.train:
raise ValueError('agent cannot be trained if constructor argument train=False')
states, actions, rewards, next_states, dones = experiences
# Get the target Q values
Q_targets_next = self.qnetwork_target.forward(next_states).detach().max(dim=1)[0].unsqueeze(1)
Q_targets = rewards + (gamma * Q_targets_next * (1 - dones))
# Get expected Q values from local model
Q_expected = self.qnetwork_local(states).gather(1, actions)
# Compute loss
loss = F.mse_loss(Q_expected, Q_targets)
# Minimize the loss
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
# ------------------- update target network ------------------- #
self.soft_update(self.qnetwork_local, self.qnetwork_target, self.tau)
@staticmethod
def soft_update(local_model, target_model, tau):
"""Soft update model parameters.
θ_target = τ*θ_local + (1 - τ)*θ_target
Args:
local_model (torch.nn.Module): weights will be copied from
target_model (torch.nn.Module): weights will be copied to
tau (float): interpolation parameter
"""
for target_param, local_param in zip(target_model.parameters(), local_model.parameters()):
# Inplace interpolation
target_param.data.copy_(tau * local_param.data + (1.0 - tau) * target_param.data)
class ReplayBuffer:
"""Fixed-size buffer to store experience tuples.
Args:
action_size (int): dimension of each action
buffer_size (int): maximum size of buffer
batch_size (int): size of each training batch
device (torch.device): device to use for tensor operations
"""
def __init__(self, action_size, buffer_size, batch_size, device):
self.action_size = action_size
self.memory = deque(maxlen=buffer_size)
self.batch_size = batch_size
self.device = device
def add(self, state, action, reward, next_state, done):
"""Add a new experience to memory."""
self.memory.append((state, action, reward, next_state, done))
def sample(self):
"""Randomly sample a batch of experiences from memory.
Returns:
tuple: tuple of vectorized sampled experiences
"""
experiences = random.sample(self.memory, k=self.batch_size)
states, actions, rewards, next_states, dones = zip(*experiences)
states = torch.from_numpy(np.vstack(states)).float().to(self.device)
actions = torch.from_numpy(np.vstack(actions)).long().to(self.device)
rewards = torch.from_numpy(np.vstack(rewards)).float().to(self.device)
next_states = torch.from_numpy(np.vstack(next_states)).float().to(self.device)
dones = torch.from_numpy(np.vstack(dones).astype(np.uint8)).float().to(self.device)
return (states, actions, rewards, next_states, dones)
def __len__(self):
"""Return the current size of internal memory."""
return len(self.memory)