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| #!/usr/bin/env python3 | |
| # -*- coding: utf-8 -*- | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| import json | |
| import streamlit as st | |
| from DDQN import DoubleDeepQNetwork | |
| from antiJamEnv import AntiJamEnv | |
| def test(jammer_type, channel_switching_cost): | |
| env = AntiJamEnv(jammer_type, channel_switching_cost) | |
| ob_space = env.observation_space | |
| ac_space = env.action_space | |
| s_size = ob_space.shape[0] | |
| a_size = ac_space.n | |
| max_env_steps = 100 | |
| TEST_Episodes = 10 | |
| 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 | |
| agentName = f'./data/DDQNAgent_{jammer_type}_csc_{channel_switching_cost}' | |
| DDQN_agent = DoubleDeepQNetwork(s_size, a_size, lr, discount_rate, epsilon, epsilon_min, epsilon_decay) | |
| DDQN_agent.model = DDQN_agent.load_saved_model(agentName) | |
| rewards = [] # Store rewards for graphing | |
| epsilons = [] # Store the Explore/Exploit | |
| # Testing agent | |
| for e_test in range(TEST_Episodes): | |
| state = env.reset() | |
| state = np.reshape(state, [1, s_size]) | |
| tot_rewards = 0 | |
| for t_test in range(max_env_steps): | |
| action = DDQN_agent.test_action(state) | |
| next_state, reward, done, _ = env.step(action) | |
| if done or t_test == max_env_steps - 1: | |
| rewards.append(tot_rewards) | |
| epsilons.append(0) # We are doing full exploit | |
| st.write(f"episode: {e_test}/{TEST_Episodes}, score: {tot_rewards}, e: {DDQN_agent.epsilon}") | |
| break | |
| next_state = np.reshape(next_state, [1, s_size]) | |
| tot_rewards += reward | |
| # DON'T STORE ANYTHING DURING TESTING | |
| state = next_state | |
| # Plotting | |
| rolling_average = np.convolve(rewards, np.ones(10) / 10, mode='valid') | |
| # Create a new Streamlit figure | |
| fig = plt.figure() | |
| plt.plot(rewards, label='Rewards') | |
| plt.plot(rolling_average, color='black', label='Rolling Average') | |
| plt.axhline(y=max_env_steps - 0.10 * max_env_steps, color='r', linestyle='-', label='Solved Line') | |
| eps_graph = [100 * x for x in epsilons] | |
| plt.plot(eps_graph, color='g', linestyle='-', label='Epsilons') | |
| plt.xlabel('Episodes') | |
| plt.ylabel('Rewards') | |
| plt.title(f'Testing Rewards - {jammer_type}, CSC: {channel_switching_cost}') | |
| plt.legend() | |
| # Display the Streamlit figure using streamlit.pyplot | |
| st.set_option('deprecation.showPyplotGlobalUse', False) | |
| st.pyplot(fig) | |
| # Save the figure | |
| plot_name = f'./data/test_rewards_{jammer_type}_csc_{channel_switching_cost}.png' | |
| plt.savefig(plot_name, bbox_inches='tight') | |
| plt.close(fig) # Close the figure to release resources | |
| # Save Results | |
| # Rewards | |
| fileName = f'./data/test_rewards_{jammer_type}_csc_{channel_switching_cost}.json' | |
| with open(fileName, 'w') as f: | |
| json.dump(rewards, f) | |