observation = env.reset()
for t in range(1000):
#env.render()
##First run through of the network. Take the observation, put it through the network
action = agent.choose_action(observation)
observation_, reward, done, info = env.step(action)
######RECORDS AND SAVES THE STATE#####
agent.store_rewards(reward)
#######################################
score += reward
agent.learn(observation, reward, observation_, done)
observation = observation_
if done:
score_history.append(score)
print("==========================================")
print("Episode: ", i_episode)
print("Reward: ", score)
break
if i_episode % 100 == 0:
agent.save_model()
print("Model Saved")
plotLearning(score_history, filename="cartpole.png", window=10)
env.close()
layer1_size=400,
layer2_size=300,
n_actions=2)
#agent.load_models()
np.random.seed(0)
score_history = []
for i in range(1000):
obs = env.reset()
done = False
score = 0
while not done:
act = agent.choose_action(obs)
new_state, reward, done, info = env.step(act)
agent.remember(obs, act, reward, new_state, int(done))
agent.learn()
score += reward
obs = new_state
#env.render()
score_history.append(score)
if i % 25 == 0:
agent.save_models()
print('episode ', i, 'score %.2f' % score,
'trailing 100 games avg %.3f' % np.mean(score_history[-100:]))
filename = 'LunarLander-alpha000025-beta00025-400-300.png'
plotLearning(score_history, filename, window=100)
input_dims=[8],
lr=0.001)
scores, eps_history = [], []
n_games = 500
for i in range(n_games):
score = 0
done = False
observation = env.reset()
while not done:
action = agent.choose_action(observation)
observation_, reward, done, info = env.step(action)
score += reward
agent.store_transition(observation, action, reward, observation_,
done)
agent.learn()
observation = observation_
#env.render()
scores.append(score)
eps_history.append(agent.epsilon)
avg_score = np.mean(scores[-100:])
print('episode ', i, 'score %.2f' % score,
'average score %.2f' % avg_score, 'epsilon %.2f' % agent.epsilon)
x = [i + 1 for i in range(n_games)]
filename = 'lunar_lander_dqn_2.png'
plotLearning(x, scores, eps_history, filename)
tau=0.001,
env=env,
batch_size=64,
layer1_size=400,
layer2_size=300,
n_actions=1)
np.random.seed(0)
score_history = []
num_epsiodes = 2500
for i in range(num_epsiodes):
obs = env.reset()
done = False
score = 0
while not done:
action = agent.choose_action(obs)
obs_, reward, done, info = env.step(action)
agent.remember(obs, action, reward, obs_, done)
agent.learn()
score += reward
obs = obs_
env.render()
score_history.append(score)
print('episode ', i, 'score %.2f' % score,
'100 game average %.2f' % np.mean(score_history[-100:]))
filename = 'pendulum.png'
x = [i for i in range(num_epsiodes)]
plotLearning(x, score_history, score_history, filename)
if done:
stepIdx = 0
if currIt + 1 < iterationNum:
env.reset()
break
throughput_history.append(throughput_sum)
rtt_history.append(rtt_sum)
score_history.append(score)
print('episode: ', i, 'score: %.2f' % score)
except KeyboardInterrupt:
print("Ctrl-C -> Exit")
finally:
env.close()
filename = 'TCP_A2C_20_step.png'
plotLearning(score_history, filename=filename, window=10)
with open('reward_20_step.csv', mode='w') as reward_file:
csv_writer = csv.writer(reward_file,
delimiter=',',
quotechar='"',
quoting=csv.QUOTE_MINIMAL)
csv_writer.writerow(score_history)
throughput_plot_filename = 'throughput_plot_20_step.png'
plotLearning(throughput_history,
filename=throughput_plot_filename,
window=10)
rtt_plot_filename = 'rtt_plot_20_step.png'
plotLearning(rtt_history, filename=rtt_plot_filename, window=10)
def main(alpha, beta, gamma):
sim_param = SimParam()
no_of_slices = sim_param.no_of_slices
no_of_users_per_slice = sim_param.no_of_users_per_slice
no_of_rb = len(sim_param.RB_pool)
no_of_timeslots = int(sim_param.T_C / sim_param.T_S)
# state space :
#n_states = no_of_slices * no_of_users_per_slice * no_of_rb * no_of_timeslots
n_states = no_of_slices
# action space : #_slices ^ #_rb
n_actions = no_of_slices ** no_of_rb
agent = Agent(alpha=alpha, beta=beta, input_dims=[n_states], gamma=gamma,
n_actions=n_actions, layer1_size=32, layer2_size=32)
env = gym.make('ransim-v1')
# run baseline algorithm
baseline_score = 0
done = False
observation = env.reset()
while not done:
action = 'baseline'
observation_, reward, done, info = env.step(action)
if done:
env.plot()
observation = observation_
baseline_score += reward
print('baseline score: %.3f' % baseline_score)
score_history = []
score = 0
num_episodes = 250
t0 = time.time()
for i in range(num_episodes):
t_tmp = time.time()
done = False
score = 0
# insert parameters
class Parameters:
pass
parameters = Parameters()
parameters.SEED_IAT = 0
parameters.SEED_SHADOWING = 0
if (i%50 ==0):
NO_logging = 0
else:
NO_logging = 1
observation = env.reset(parameters, NO_logging)
while not done:
action = agent.choose_action(observation)
observation_, reward, done, info = env.step(action)
if done & (i%50 ==0):
env.plot()
agent.learn(observation, reward, observation_, done)
observation = observation_
score += reward#.sum()
score_history.append(score)
elapsed_time = time.time() - t_tmp
print('episode: ', i,'score: %.3f time: %d' % (score,elapsed_time))
print(time.time()-t0)
filename = 'results/result_alpha_%.4f_beta_%.4f_gamma_%.2f.png' %(alpha, beta, gamma)
#filename = 'cartpole-discrete-actor-critic-alpha0001-beta0005-32x32fc-1500games.png'
plotLearning(score_history, filename=filename, window=10)
epsHistory.append(brain.EPSILON)
done = False
observation = env.reset()
frames = [np.sum(observation[15:200, 30:125], axis=2)]
score = 0
lastAction = 0
while not done:
if len(frames) == 3:
action = brain.chooseAction(frames)
frames = []
else:
action = lastAction
observation_, reward, done, info = env.step(action)
score += reward
frames.append(np.sum(observation_[15:200, 30:125], axis=2))
if done and info['ale.lives'] == 0:
reward = -100
brain.storeTransition(
np.mean(observation[15:200, 30:125], axis=2), action, reward,
np.mean(observation_[15:200, 30:125], axis=2))
observation = observation_
brain.learn(batch_size)
lastAction = action
#env.render(
scores.append(score)
print('score:', score)
x = [i + 1 for i in range(numGames)]
fileName = str(numGames) + 'Games' + 'Gamma' + str(brain.GAMMA) + \
'Alpha' + str(brain.ALPHA) + 'Memory' + str(brain.memSize)+ '.png'
plotLearning(x, scores, epsHistory, fileName)
