def test_process(config, steps, target_actor):
env = NormalizedEnv(gym.make('Pendulum-v0'))
agent = Action(state_dim=env.observation_space.shape[0],
action_dim=env.action_space.shape[0])
reward_list = []
try:
while True:
# for test
if (steps.value) != 0 and (steps.value % config.test_every_eposide
== 0):
agent.load_param(target_actor)
print("test agent load param ")
et_reward = 0
for index in range(config.num_eposide_test):
eposide = 0
state = env.reset()
state = (state - env.observation_space.low) / (
env.observation_space.high - env.observation_space.low)
while True:
action = agent.chose_action(state, explort=False)
next_state, reward, done, _ = env.step(action)
env.render()
next_state = (next_state - env.observation_space.low
) / (env.observation_space.high -
env.observation_space.low)
eposide += reward
state = next_state
if done:
break
et_reward += eposide
print("\033[93m [ test ] eposide average reward : {}\033[00m".
format(et_reward / config.num_eposide_test))
reward_list.append(et_reward / config.num_eposide_test)
x = np.arange(len(reward_list))
y = np.array(reward_list)
plt.plot(x, y)
plt.savefig("./eposide_reward.png")
except Exception as e:
print(e)
print("test process exit")
env.close()
def main():
env = NormalizedEnv(gym.make('Pendulum-v0'))
state_dim = env.observation_space.shape[0]
action_dim = env.action_space.shape[0]
agent = Actor(state_dim, action_dim).to('cuda')
agent.load_state_dict(torch.load('./Models/78.0_actor.pt'))
eposide = 0
done = False
eposide_list = []
while eposide < 100:
eposide_reward = 0
state = env.reset()
state = (state - env.observation_space.low) / (
env.observation_space.high - env.observation_space.low)
state = to_tensor(state)
while not done:
action = agent.forward(state).detach().cpu().data.numpy()
state_, reward, done, _ = env.step(action)
state_ = (state_ - env.observation_space.low) / (
env.observation_space.high - env.observation_space.low)
env.render()
state = to_tensor(state_)
eposide_reward += reward
eposide_list.append(eposide_reward)
eposide += 1
done = False
print("{} : {}".format(eposide, eposide_reward))
import matplotlib.pyplot as plt
x = np.arange(100)
y = np.array(eposide_list)
plt.plot(x, y)
plt.savefig("./test_eposide_reward.png")
env.close()
def main(args):
env = make_env('simple_tag')
env = NormalizedEnv(env)
kwargs = dict()
kwargs['config'] = args
predator_model = Predators(16, 2, num_agent=3, **kwargs)
preyer_model = Preyer(14, 2, **kwargs)
if args.tensorboard:
writer = SummaryWriter(log_dir='runs/' + args.log_dir)
episode = 0
total_step = 0
while episode < args.max_episodes:
state = env.reset()
episode += 1
step = 0
predator_accum_reward = []
preyer_accum_reward = 0
while True:
state_predator, state_prayer = split_obs(state)
predator_model.prep_eval()
action_predator = predator_model.choose_action(state_predator)
action_prayer = preyer_model.random_action()
#action_prayer = preyer_model.choose_action(state_prayer)
action = merge_action(action_predator, action_prayer)
next_state, reward, done, info = env.step(action)
step += 1
total_step += 1
predator_accum_reward.append(np.mean(reward[:3]))
preyer_accum_reward = reward[3]
if step > args.episode_length:
done = [True, True, True, True]
if args.render and (episode % 10 == 1):
env.render(mode='rgb_array')
predator_model.memory(state[:3], action[:3], reward[:3],
next_state[:3], done[:3])
# preyer_model.memory(state[3], action[3], reward[3], next_state[3], done[3])
if len(
predator_model.replay_buffer
) >= args.batch_size and total_step % args.steps_per_update == 0:
predator_model.prep_train()
predator_model.train()
# preyer_model.train()
if True in done:
predator_c_loss, predator_a_loss = predator_model.getLoss()
preyer_c_loss, preyer_a_loss = preyer_model.getLoss()
print("[Episode %05d] reward_predator %3.1f reward_preyer %3.1f predator_c_loss %3.1f predator_a_loss %3.1f preyer_c_loss %3.1f preyer_a_loss %3.1f" % \
(episode, np.mean(predator_accum_reward).item(), preyer_accum_reward, predator_c_loss, predator_a_loss, preyer_c_loss, preyer_a_loss))
if args.tensorboard:
# writer.add_scalar(tag='debug/memory_length', global_step=episode, scalar_value=len(predator_model.replay_buffer))
# writer.add_scalar(tag='debug/predator_epsilon', global_step=episode, scalar_value=predator_model.epsilon)
# writer.add_scalar(tag='debug/preyer_epsilon', global_step=episode, scalar_value=preyer_model.epsilon)
writer.add_scalar(
tag='agent/reward_predator',
global_step=episode,
scalar_value=np.mean(predator_accum_reward).item())
# writer.add_scalar(tag='perf/reward_preyer', global_step=episode, scalar_value=preyer_accum_reward)
if predator_c_loss and predator_a_loss:
writer.add_scalars('agent/predator_loss',
global_step=episode,
tag_scalar_dict={
'actor': -predator_a_loss,
'critic': predator_c_loss
})
# writer.add_scalar(tag='loss/preyer_c_loss', global_step=episode, scalar_value=preyer_c_loss)
# writer.add_scalar(tag='loss/preyer_a_loss', global_step=episode, scalar_value=preyer_a_loss)
predator_model.reset()
preyer_model.reset()
break
state = next_state
if args.tensorboard:
writer.close()