File size: 1,540 Bytes
f3eaffd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 | import numpy as np
from rlbench.action_modes.action_mode import MoveArmThenGripper
from rlbench.action_modes.arm_action_modes import JointVelocity
from rlbench.action_modes.gripper_action_modes import Discrete
from rlbench.environment import Environment
from rlbench.observation_config import ObservationConfig
from rlbench.tasks import MT30_V1
class Agent(object):
def __init__(self, action_shape):
self.action_shape = action_shape
def act(self, obs):
arm = np.random.normal(0.0, 0.1, size=(self.action_shape[0] - 1,))
gripper = [1.0] # Always open
return np.concatenate([arm, gripper], axis=-1)
obs_config = ObservationConfig()
obs_config.set_all(True)
env = Environment(
action_mode=MoveArmThenGripper(
arm_action_mode=JointVelocity(), gripper_action_mode=Discrete()),
obs_config=ObservationConfig(),
headless=False)
env.launch()
agent = Agent(env.action_shape)
train_tasks = MT30_V1['train']
training_cycles_per_task = 3
training_steps_per_task = 80
episode_length = 40
for _ in range(training_cycles_per_task):
task_to_train = np.random.choice(train_tasks, 1)[0]
task = env.get_task(task_to_train)
task.sample_variation() # random variation
for i in range(training_steps_per_task):
if i % episode_length == 0:
print('Reset Episode')
descriptions, obs = task.reset()
print(descriptions)
action = agent.act(obs)
obs, reward, terminate = task.step(action)
print('Done')
env.shutdown()
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