def main():
# spin up environment
env = TwoAreaPowerSystemEnv()
env.seed(2)
state = env.reset()
# spin up agent
agent = DdpgController(state_size=7, action_size=2, random_seed=2)
# spin up the power demand signal
signal = StepSignal()
signal.reset(15, 'on', 0.01, 'off', 0.0)
# Load the actor and critic networks
agent.actor_local.load_state_dict(torch.load('checkpoint_actor.pth'))
agent.critic_local.load_state_dict(torch.load('checkpoint_critic.pth'))
# initialise empty list to store simulation output
out_s_1 = [0]
out_s_2 = [0]
out_tieline = [0]
control_s_1 = [0]
control_s_2 = [0]
demand_list = [0]
time_list = [0]
score = 0
while True:
action = agent.act(state, add_noise=False)
demand = (signal.del_p_L_1_func(env.t), signal.del_p_L_2_func(env.t))
state, reward, done, _ = env.step(action, demand)
score += reward
out_s_1.append(state[2])
out_s_2.append(state[6])
out_tieline.append(state[3])
control_s_1.append(action[0])
control_s_2.append(action[1])
demand_list.append(demand[0])
time_list.append(env.t)
if done:
break
print('Score: {}'.format(score))
# Plot the agent performance png files
png_plot_file_path = './test_plots/ddpg_test_plot/pngplot/zz_plot_final.png'
pik_file_path = './test_plots/ddpg_test_plot/pickledata/zz_plot_final.pkl'
capture_agent_progress(time_list, out_s_1, out_s_2, control_s_1,
control_s_2, out_tieline, demand_list,
png_plot_file_path, pik_file_path)
def main():
# spin up environment
env = TwoAreaPowerSystemEnv()
env.seed(2)
# spin up ddpg agent
agent_ddpg = DdpgController(state_size=7, action_size=2, random_seed=2)
# spin up pi agent
agent_pi = ClassicalPiController()
# bundle agents
agent_list = [agent_ddpg, agent_pi]
####################################################
# COMMENT OUT IF NOT CONTINUING TRAINING
####################################################
# Load the actor and critic networks
#agent.actor_local.load_state_dict(torch.load('checkpoint_actor.pth'))
#agent.critic_local.load_state_dict(torch.load('checkpoint_critic.pth'))
# spin up the power demand signal
signal = StepSignal()
# train the agent
scores = ddpg_train(env, agent_list, signal)
def main():
# spin up environment
env = TwoAreaPowerSystemEnv()
env.seed(2)
state = env.reset()
# spin up agent
agent = DdpgController(state_size=7, action_size=2, random_seed=2)
# spin up the power demand signal
signal = StepSignal()
signal.reset(1, 'on', 0.01, 'off', 0.0)
# Load the actor and critic networks
agent.actor_local.load_state_dict(torch.load('checkpoint_actor.pth'))
agent.critic_local.load_state_dict(torch.load('checkpoint_critic.pth'))
# initialise empty list to store simulation output
out_s_1 = [0]
out_s_2 = [0]
control_s_1 = [0]
control_s_2 = [0]
demand_list = [0]
time_list = [0]
score = 0
while True:
action = agent.act(state, add_noise=False)
demand = (signal.del_p_L_1_func(env.t), signal.del_p_L_2_func(env.t))
state, reward, done, _ = env.step(action, demand)
score += reward
out_s_1.append(state[2])
out_s_2.append(state[6])
time_list.append(env.t)
control_s_1.append(action[0])
control_s_2.append(action[1])
demand_list.append(demand[0])
if done:
break
print('Score: {}'.format(score))
plt.subplot(411)
plt.plot(time_list, out_s_1)
plt.plot(time_list, out_s_2)
plt.subplot(412)
plt.plot(time_list, control_s_1)
plt.subplot(413)
plt.plot(time_list, control_s_2)
plt.subplot(414)
plt.plot(time_list, demand_list)
plt.show()