def test_es_compilation(self):
"""Test whether an ESTrainer can be built on all frameworks."""
ray.init(num_cpus=4)
config = es.DEFAULT_CONFIG.copy()
# Keep it simple.
config["model"]["fcnet_hiddens"] = [10]
config["model"]["fcnet_activation"] = None
config["noise_size"] = 2500000
config["num_workers"] = 1
config["episodes_per_batch"] = 10
config["train_batch_size"] = 100
# Test eval workers ("normal" Trainer eval WorkerSet).
config["evaluation_interval"] = 1
config["evaluation_num_workers"] = 2
num_iterations = 1
for _ in framework_iterator(config):
for env in ["CartPole-v0", "Pendulum-v0"]:
plain_config = config.copy()
trainer = es.ESTrainer(config=plain_config, env=env)
for i in range(num_iterations):
results = trainer.train()
print(results)
check_compute_single_action(trainer)
trainer.stop()
ray.shutdown()
def test_es_compilation(self):
"""Test whether an ESTrainer can be built on all frameworks."""
ray.init()
config = es.DEFAULT_CONFIG.copy()
# Keep it simple.
config["model"]["fcnet_hiddens"] = [10]
config["model"]["fcnet_activation"] = None
num_iterations = 2
for _ in framework_iterator(config, ("torch", "tf")):
plain_config = config.copy()
trainer = es.ESTrainer(config=plain_config, env="CartPole-v0")
for i in range(num_iterations):
results = trainer.train()
print(results)
def test_es_compilation(self):
"""Test whether an ESTrainer can be built on all frameworks."""
ray.init()
config = es.DEFAULT_CONFIG.copy()
# Keep it simple.
config["model"]["fcnet_hiddens"] = [10]
config["model"]["fcnet_activation"] = None
config["noise_size"] = 2500000
num_iterations = 2
for _ in framework_iterator(config):
plain_config = config.copy()
trainer = es.ESTrainer(config=plain_config, env="CartPole-v0")
for i in range(num_iterations):
results = trainer.train()
print(results)
check_compute_single_action(trainer)
trainer.stop()
ray.shutdown()
def render(checkpoint, home_path):
"""
Renders pybullet and mujoco environments.
"""
alg = re.match('.+?(?=_)', os.path.basename(os.path.normpath(home_path))).group(0)
current_env = re.search("(?<=_).*?(?=_)", os.path.basename(os.path.normpath(home_path))).group(0)
checkpoint_path = home_path + "checkpoint_" + str(checkpoint) + "/checkpoint-" + str(checkpoint)
config = json.load(open(home_path + "params.json"))
config_bin = pickle.load(open(home_path + "params.pkl", "rb"))
ray.shutdown()
import pybullet_envs
ray.init()
ModelCatalog.register_custom_model("RBF", RBFModel)
ModelCatalog.register_custom_model("MLP_2_64", MLP)
ModelCatalog.register_custom_model("linear", Linear)
if alg == "PPO":
trainer = ppo.PPOTrainer(config_bin)
if alg == "SAC":
trainer = sac.SACTrainer(config)
if alg == "DDPG":
trainer = ddpg.DDPGTrainer(config)
if alg == "PG":
trainer = pg.PGTrainer(config)
if alg == "A3C":
trainer = a3c.A3CTrainer(config)
if alg == "TD3":
trainer = td3.TD3Trainer(config)
if alg == "ES":
trainer = es.ESTrainer(config)
if alg == "ARS":
trainer = ars.ARSTrainer(config)
# "normalize_actions": true,
trainer.restore(checkpoint_path)
if "Bullet" in current_env:
env = gym.make(current_env, render=True)
else:
env = gym.make(current_env)
#env.unwrapped.reset_model = det_reset_model
env._max_episode_steps = 10000
obs = env.reset()
action_hist = []
m_act_hist = []
state_hist = []
obs_hist = []
reward_hist = []
done = False
step = 0
for t in range(10000):
# for some algorithms you can get the sample mean out, need to change the value on the index to match your env for now
# mean_actions = out_dict['behaviour_logits'][:17]
# actions = trainer.compute_action(obs.flatten())
# sampled_actions, _ , out_dict = trainer.compute_action(obs.flatten(),full_fetch=True)
sampled_actions = trainer.compute_action(obs.flatten())
# sampled_actions, _ , out_dict = trainer.compute_action(obs.flatten(),full_fetch=True)
actions = sampled_actions
obs, reward, done, _ = env.step(np.asarray(actions))
# env.camera_adjust()
env.render(mode='human')
time.sleep(0.01)
# env.render()
# env.render(mode='rgb_array', close = True)
# p.computeViewMatrix(cameraEyePosition=[0,10,5], cameraTargetPosition=[0,0,0], cameraUpVector=[0,0,0])
# if step % 1000 == 0:
# env.reset()
# step += 1
action_hist.append(np.copy(actions))
obs_hist.append(np.copy(obs))
reward_hist.append(np.copy(reward))
if done:
obs = env.reset()
# print(sum(reward_hist))
# print((obs_hist))
#plt.plot(action_hist)
#plt.figure()
#plt.figure()
#plt.plot(obs_hist)
#plt.figure()
# Reminder that the bahavior logits that come out are the mean and logstd (not log mean, despite the name logit)
# trainer.compute_action(obs, full_fetch=True)
trainer.compute_action(obs)