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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt
import json
from DDQN import DoubleDeepQNetwork
from antiJamEnv import AntiJamEnv
def train(jammer_type, channel_switching_cost):
env = AntiJamEnv(jammer_type, channel_switching_cost)
ob_space = env.observation_space
ac_space = env.action_space
print("Observation space: ", ob_space, ob_space.dtype)
print("Action space: ", ac_space, ac_space.n)
s_size = ob_space.shape[0]
a_size = ac_space.n
max_env_steps = 100
TRAIN_Episodes = 100
env._max_episode_steps = max_env_steps
epsilon = 1.0 # exploration rate
epsilon_min = 0.01
epsilon_decay = 0.999
discount_rate = 0.95
lr = 0.001
batch_size = 32
DDQN_agent = DoubleDeepQNetwork(s_size, a_size, lr, discount_rate, epsilon, epsilon_min, epsilon_decay)
rewards = [] # Store rewards for graphing
epsilons = [] # Store the Explore/Exploit
# Training agent
for e in range(TRAIN_Episodes):
state = env.reset()
# print(f"Initial state is: {state}")
state = np.reshape(state, [1, s_size]) # Resize to store in memory to pass to .predict
tot_rewards = 0
previous_action = 0
for time in range(max_env_steps): # 200 is when you "solve" the game. This can continue forever as far as I know
action = DDQN_agent.action(state)
next_state, reward, done, _ = env.step(action)
# print(f'The next state is: {next_state}')
# done: Three collisions occurred in the last 10 steps.
# time == max_env_steps - 1 : No collisions occurred
if done or time == max_env_steps - 1:
rewards.append(tot_rewards)
epsilons.append(DDQN_agent.epsilon)
print("episode: {}/{}, score: {}, e: {}"
.format(e, TRAIN_Episodes, tot_rewards, DDQN_agent.epsilon))
break
# Applying channel switching cost
next_state = np.reshape(next_state, [1, s_size])
tot_rewards += reward
DDQN_agent.store(state, action, reward, next_state, done) # Resize to store in memory to pass to .predict
state = next_state
# Experience Replay
if len(DDQN_agent.memory) > batch_size:
DDQN_agent.experience_replay(batch_size)
# Update the weights after each episode (You can configure this for x steps as well
DDQN_agent.update_target_from_model()
# If our current NN passes we are done
# Early stopping criteria: I am going to use the last 10 runs within 1% of the max
if len(rewards) > 10 and np.average(rewards[-10:]) >= max_env_steps - 0.10 * max_env_steps:
break
# Plotting
plotName = f'results/train/rewards_{jammer_type}_csc_{channel_switching_cost}.png'
rolling_average = np.convolve(rewards, np.ones(10) / 10)
plt.plot(rewards)
plt.plot(rolling_average, color='black')
plt.axhline(y=max_env_steps - 0.10 * max_env_steps, color='r', linestyle='-') # Solved Line
# Scale Epsilon (0.001 - 1.0) to match reward (0 - 100) range
eps_graph = [100 * x for x in epsilons]
plt.plot(eps_graph, color='g', linestyle='-')
plt.xlabel('Episodes')
plt.ylabel('Rewards')
plt.savefig(plotName, bbox_inches='tight')
plt.show()
# Save Results
# Rewards
fileName = f'results/train/rewards_{jammer_type}_csc_{channel_switching_cost}.json'
with open(fileName, 'w') as f:
json.dump(rewards, f)
# Save the agent as a SavedAgent.
agentName = f'savedAgents/DDQNAgent_{jammer_type}_csc_{channel_switching_cost}'
DDQN_agent.save_model(agentName)
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