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| #!/usr/bin/env python3 | |
| # -*- coding: utf-8 -*- | |
| import os | |
| from os import mkdir | |
| import gym | |
| import tensorflow as tf | |
| import tf_slim as slim | |
| import numpy as np | |
| import matplotlib as mpl | |
| import matplotlib.pyplot as plt | |
| import json | |
| from tensorflow import keras | |
| 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) | |