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DQNAgent.py
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# checkout https://github.com/philtabor/Deep-Q-Learning-Paper-To-Code/tree/master/DDQN
# I didn't make many changes, I simply ensured it fits with the CybORG BaseAgent
import inspect
from CybORG import CybORG
from CybORG.Agents.SimpleAgents.BaseAgent import BaseAgent
from CybORG.Agents.Wrappers.ChallengeWrapper import ChallengeWrapper
from CybORG.Agents.Wrappers.FixedFlatWrapper import FixedFlatWrapper
from CybORG.Agents.Wrappers.OpenAIGymWrapper import OpenAIGymWrapper
from CybORG.Agents.Wrappers.ReduceActionSpaceWrapper import ReduceActionSpaceWrapper
from CybORG.Agents.SimpleAgents.BaseAgent import BaseAgent
import torch as T
import numpy as np
from DQN.DeepQNetwork import DeepQNetwork, DeepRNNNetwork
from DQN.ReplayBuffer import ReplayBuffer
class DQNAgent(BaseAgent):
def __init__(self, gamma=0.9, epsilon=0, lr=0.1, n_actions=41, input_dims=(52,),
mem_size=1000, batch_size=32, eps_min=0.01, eps_dec=5e-7,
replace=1000, algo='DDQN', env_name='Scenario1b', chkpt_dir='chkpt', load=False):
self.gamma = gamma
self.epsilon = epsilon
self.lr = lr
self.n_actions = n_actions
self.input_dims = input_dims
self.batch_size = batch_size
self.eps_min = eps_min
self.eps_dec = eps_dec
self.replace_target_cnt = replace
self.algo = algo
self.env_name = env_name
self.chkpt_dir = chkpt_dir
self.action_space = [i for i in range(n_actions)]
self.learn_step_counter = 0
self.memory = ReplayBuffer(mem_size, input_dims, n_actions)
self.q_eval = DeepQNetwork(self.lr, self.n_actions,
input_dims=self.input_dims,
name=self.env_name+'_'+self.algo+'_q_eval',
chkpt_dir=self.chkpt_dir)
self.q_next = DeepQNetwork(self.lr, self.n_actions,
input_dims=self.input_dims,
name=self.env_name+'_'+self.algo+'_q_next',
chkpt_dir=self.chkpt_dir)
# if epsilon=0 it will just use the model
def get_action(self, observation, action_space, ignore_epsilon=False):
if ignore_epsilon or (np.random.random() > self.epsilon):
state = T.tensor([observation], dtype=T.float).to(self.q_eval.device)
actions = self.q_eval.forward(state)
action = T.argmax(actions).item()
else:
action = np.random.choice(self.action_space)
return action
def store_transition(self, state, action, reward, state_, done):
self.memory.store_transition(state, action, reward, state_, done)
def sample_memory(self):
state, action, reward, new_state, done = \
self.memory.sample_buffer(self.batch_size)
states = T.tensor(state).to(self.q_eval.device)
rewards = T.tensor(reward).to(self.q_eval.device)
dones = T.tensor(done).to(self.q_eval.device)
actions = T.tensor(action).to(self.q_eval.device)
states_ = T.tensor(new_state).to(self.q_eval.device)
return states, actions, rewards, states_, dones
def replace_target_network(self):
if self.replace_target_cnt is not None and \
self.learn_step_counter % self.replace_target_cnt == 0:
self.q_next.load_state_dict(self.q_eval.state_dict())
def decrement_epsilon(self):
self.epsilon = self.epsilon - self.eps_dec \
if self.epsilon > self.eps_min else self.eps_min
def train(self):
if self.memory.mem_cntr < self.batch_size:
return
self.q_eval.optimizer.zero_grad()
self.replace_target_network()
states, actions, rewards, states_, dones = self.sample_memory()
indices = np.arange(self.batch_size)
q_pred = self.q_eval.forward(states)[indices, actions]
q_next = self.q_next.forward(states_)
q_eval = self.q_eval.forward(states_)
max_actions = T.argmax(q_eval, dim=1)
q_next[dones] = 0.0
q_target = rewards + self.gamma*q_next[indices, max_actions]
loss = self.q_eval.loss(q_target, q_pred).to(self.q_eval.device)
loss.backward()
self.q_eval.optimizer.step()
self.learn_step_counter += 1
self.decrement_epsilon()
def end_episode(self):
pass
def set_initial_values(self, action_space, observation):
pass
def save_models(self):
self.q_eval.save_checkpoint()
self.q_next.save_checkpoint()
def load_models(self):
self.q_eval.load_checkpoint()
self.q_next.load_checkpoint()
class RNNDQNAgent(DQNAgent):
def __init__(self, gamma=0.99, epsilon=1, lr=0.0001, n_actions=10, input_dims=(10), lookback_steps=7,
mem_size=1000, batch_size=64, eps_min=0.01, eps_dec=5e-7, hid_size=64,
replace=1000, algo=None, env_name=None, chkpt_dir='chkpt', load=False,
env=None):
self.lookback_steps = lookback_steps
super(RNNDQNAgent, self).__init__(gamma=gamma, epsilon=epsilon, lr=lr, n_actions=n_actions,
input_dims=input_dims,
mem_size=mem_size, batch_size=batch_size, eps_min=eps_min, eps_dec=eps_dec,
replace=replace, algo=algo, env_name=env_name, chkpt_dir=chkpt_dir)
self.memory = ReplayBuffer(mem_size, (self.lookback_steps, input_dims[0]), n_actions)
self.q_eval = DeepRNNNetwork(self.lr, self.n_actions,
input_dims=self.input_dims,
name=self.env_name+'_'+self.algo+'_q_eval',
chkpt_dir=self.chkpt_dir, hid_size=hid_size)
self.q_next = DeepRNNNetwork(self.lr, self.n_actions,
input_dims=self.input_dims,
name=self.env_name+'_'+self.algo+'_q_next',
chkpt_dir=self.chkpt_dir, hid_size=hid_size)
self.observation_buffer = np.zeros((self.lookback_steps, self.input_dims[0]))
if load:
self.load_models()
def get_action(self, observation, action_space):
if (observation.shape) != self.observation_buffer.shape:
self.observation_buffer[:-1] = self.observation_buffer[1:]
self.observation_buffer[-1] = observation
else:
self.observation_buffer = observation
if np.random.random() > self.epsilon:
state = T.tensor([self.observation_buffer], dtype=T.float).to(self.q_eval.device)
actions = self.q_eval.forward(state)
action = T.argmax(actions).item()
else:
action = np.random.choice(self.action_space)
return action