current state
This commit is contained in:
Fabian
@@ -1,152 +0,0 @@
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import os
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import numpy as np
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from gym import utils
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from gym.envs.mujoco import MujocoEnv
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import alr_envs.utils.utils as alr_utils
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class ALRReacherEnv(MujocoEnv, utils.EzPickle):
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def __init__(self, steps_before_reward: int = 200, n_links: int = 5, ctrl_cost_weight: int = 1,
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balance: bool = False):
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utils.EzPickle.__init__(**locals())
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self._steps = 0
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self.steps_before_reward = steps_before_reward
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self.n_links = n_links
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self.balance = balance
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self.balance_weight = 1.0
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self.ctrl_cost_weight = ctrl_cost_weight
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self.reward_weight = 1
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if steps_before_reward == 200:
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self.reward_weight = 200
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elif steps_before_reward == 50:
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self.reward_weight = 50
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if n_links == 5:
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file_name = 'reacher_5links.xml'
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elif n_links == 7:
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file_name = 'reacher_7links.xml'
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else:
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raise ValueError(f"Invalid number of links {n_links}, only 5 or 7 allowed.")
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MujocoEnv.__init__(self, os.path.join(os.path.dirname(__file__), "assets", file_name), 2)
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def step(self, a):
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self._steps += 1
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reward_dist = 0.0
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angular_vel = 0.0
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reward_balance = 0.0
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is_delayed = self.steps_before_reward > 0
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reward_ctrl = - np.square(a).sum() * self.ctrl_cost_weight
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if self._steps >= self.steps_before_reward:
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vec = self.get_body_com("fingertip") - self.get_body_com("target")
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reward_dist -= self.reward_weight * np.linalg.norm(vec)
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if is_delayed:
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# avoid giving this penalty for normal step based case
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# angular_vel -= 10 * np.linalg.norm(self.sim.data.qvel.flat[:self.n_links])
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angular_vel -= 10 * np.square(self.sim.data.qvel.flat[:self.n_links]).sum()
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# if is_delayed:
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# # Higher control penalty for sparse reward per timestep
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# reward_ctrl *= 10
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if self.balance:
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reward_balance -= self.balance_weight * np.abs(
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alr_utils.angle_normalize(np.sum(self.sim.data.qpos.flat[:self.n_links]), type="rad"))
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reward = reward_dist + reward_ctrl + angular_vel + reward_balance
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self.do_simulation(a, self.frame_skip)
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ob = self._get_obs()
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done = False
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return ob, reward, done, dict(reward_dist=reward_dist, reward_ctrl=reward_ctrl,
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velocity=angular_vel, reward_balance=reward_balance,
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end_effector=self.get_body_com("fingertip").copy(),
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goal=self.goal if hasattr(self, "goal") else None)
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def viewer_setup(self):
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self.viewer.cam.trackbodyid = 0
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# def reset_model(self):
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# qpos = self.init_qpos
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# if not hasattr(self, "goal"):
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# self.goal = np.array([-0.25, 0.25])
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# # self.goal = self.init_qpos.copy()[:2] + 0.05
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# qpos[-2:] = self.goal
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# qvel = self.init_qvel
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# qvel[-2:] = 0
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# self.set_state(qpos, qvel)
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# self._steps = 0
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#
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# return self._get_obs()
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def reset_model(self):
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qpos = self.init_qpos.copy()
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while True:
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# full space
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# self.goal = self.np_random.uniform(low=-self.n_links / 10, high=self.n_links / 10, size=2)
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# I Quadrant
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# self.goal = self.np_random.uniform(low=0, high=self.n_links / 10, size=2)
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# II Quadrant
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# self.goal = np.random.uniform(low=[-self.n_links / 10, 0], high=[0, self.n_links / 10], size=2)
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# II + III Quadrant
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# self.goal = np.random.uniform(low=-self.n_links / 10, high=[0, self.n_links / 10], size=2)
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# I + II Quadrant
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self.goal = np.random.uniform(low=[-self.n_links / 10, 0], high=self.n_links, size=2)
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if np.linalg.norm(self.goal) < self.n_links / 10:
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break
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qpos[-2:] = self.goal
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qvel = self.init_qvel.copy()
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qvel[-2:] = 0
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self.set_state(qpos, qvel)
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self._steps = 0
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return self._get_obs()
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# def reset_model(self):
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# qpos = self.np_random.uniform(low=-0.1, high=0.1, size=self.model.nq) + self.init_qpos
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# while True:
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# self.goal = self.np_random.uniform(low=-self.n_links / 10, high=self.n_links / 10, size=2)
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# if np.linalg.norm(self.goal) < self.n_links / 10:
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# break
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# qpos[-2:] = self.goal
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# qvel = self.init_qvel + self.np_random.uniform(low=-.005, high=.005, size=self.model.nv)
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# qvel[-2:] = 0
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# self.set_state(qpos, qvel)
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# self._steps = 0
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#
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# return self._get_obs()
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def _get_obs(self):
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theta = self.sim.data.qpos.flat[:self.n_links]
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target = self.get_body_com("target")
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return np.concatenate([
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np.cos(theta),
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np.sin(theta),
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target[:2], # x-y of goal position
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self.sim.data.qvel.flat[:self.n_links], # angular velocity
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self.get_body_com("fingertip") - target, # goal distance
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[self._steps],
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])
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if __name__ == '__main__':
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nl = 5
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render_mode = "human" # "human" or "partial" or "final"
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env = ALRReacherEnv(n_links=nl)
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obs = env.reset()
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for i in range(2000):
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# objective.load_result("/tmp/cma")
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# test with random actions
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ac = env.action_space.sample()
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obs, rew, d, info = env.step(ac)
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if i % 10 == 0:
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env.render(mode=render_mode)
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if d:
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env.reset()
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env.close()
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@@ -1,53 +0,0 @@
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import os
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import numpy as np
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from gym import utils
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from gym.envs.mujoco import mujoco_env
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import alr_envs.utils.utils as alr_utils
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class BalancingEnv(mujoco_env.MujocoEnv, utils.EzPickle):
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def __init__(self, n_links=5):
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utils.EzPickle.__init__(**locals())
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self.n_links = n_links
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if n_links == 5:
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file_name = 'reacher_5links.xml'
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elif n_links == 7:
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file_name = 'reacher_7links.xml'
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else:
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raise ValueError(f"Invalid number of links {n_links}, only 5 or 7 allowed.")
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mujoco_env.MujocoEnv.__init__(self, os.path.join(os.path.dirname(__file__), "assets", file_name), 2)
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def step(self, a):
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angle = alr_utils.angle_normalize(np.sum(self.sim.data.qpos.flat[:self.n_links]), type="rad")
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reward = - np.abs(angle)
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self.do_simulation(a, self.frame_skip)
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ob = self._get_obs()
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done = False
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return ob, reward, done, dict(angle=angle, end_effector=self.get_body_com("fingertip").copy())
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def viewer_setup(self):
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self.viewer.cam.trackbodyid = 1
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def reset_model(self):
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# This also generates a goal, we however do not need/use it
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qpos = self.np_random.uniform(low=-0.1, high=0.1, size=self.model.nq) + self.init_qpos
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qpos[-2:] = 0
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qvel = self.init_qvel + self.np_random.uniform(low=-.005, high=.005, size=self.model.nv)
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qvel[-2:] = 0
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self.set_state(qpos, qvel)
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return self._get_obs()
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def _get_obs(self):
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theta = self.sim.data.qpos.flat[:self.n_links]
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return np.concatenate([
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np.cos(theta),
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np.sin(theta),
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self.sim.data.qvel.flat[:self.n_links], # this is angular velocity
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])
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@@ -2,7 +2,7 @@ from typing import Union, Tuple
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import numpy as np
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from alr_envs.mp.raw_interface_wrapper import RawInterfaceWrapper
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from alr_envs.black_box.raw_interface_wrapper import RawInterfaceWrapper
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class MPWrapper(RawInterfaceWrapper):
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@@ -0,0 +1,105 @@
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import os
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import numpy as np
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from gym import utils
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from gym.envs.mujoco import MujocoEnv
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class ReacherEnv(MujocoEnv, utils.EzPickle):
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"""
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More general version of the gym mujoco Reacher environment
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"""
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def __init__(self, sparse: bool = False, n_links: int = 5, ctrl_cost_weight: int = 1):
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utils.EzPickle.__init__(**locals())
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self._steps = 0
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self.n_links = n_links
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self.ctrl_cost_weight = ctrl_cost_weight
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self.sparse = sparse
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self.reward_weight = 1 if not sparse else 200
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file_name = f'reacher_{n_links}links.xml'
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MujocoEnv.__init__(self, os.path.join(os.path.dirname(__file__), "assets", file_name), 2)
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def step(self, action):
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self._steps += 1
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is_reward = not self.sparse or (self.sparse and self._steps == 200)
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reward_dist = 0.0
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angular_vel = 0.0
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if is_reward:
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reward_dist = self.distance_reward()
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angular_vel = self.velocity_reward()
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reward_ctrl = -self.ctrl_cost_weight * np.square(action).sum()
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reward = reward_dist + reward_ctrl + angular_vel
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self.do_simulation(action, self.frame_skip)
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ob = self._get_obs()
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done = False
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infos = dict(
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reward_dist=reward_dist,
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reward_ctrl=reward_ctrl,
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velocity=angular_vel,
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end_effector=self.get_body_com("fingertip").copy(),
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goal=self.goal if hasattr(self, "goal") else None
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)
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return ob, reward, done, infos
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def distance_reward(self):
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vec = self.get_body_com("fingertip") - self.get_body_com("target")
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return -self.reward_weight * np.linalg.norm(vec)
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def velocity_reward(self):
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return -10 * np.square(self.sim.data.qvel.flat[:self.n_links]).sum() if self.sparse else 0.0
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def viewer_setup(self):
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self.viewer.cam.trackbodyid = 0
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def reset_model(self):
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qpos = (
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# self.np_random.uniform(low=-0.1, high=0.1, size=self.model.nq) +
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self.init_qpos.copy()
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)
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while True:
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# full space
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self.goal = self.np_random.uniform(low=-self.n_links / 10, high=self.n_links / 10, size=2)
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# I Quadrant
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# self.goal = self.np_random.uniform(low=0, high=self.n_links / 10, size=2)
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# II Quadrant
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# self.goal = np.random.uniform(low=[-self.n_links / 10, 0], high=[0, self.n_links / 10], size=2)
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# II + III Quadrant
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# self.goal = np.random.uniform(low=-self.n_links / 10, high=[0, self.n_links / 10], size=2)
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# I + II Quadrant
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# self.goal = np.random.uniform(low=[-self.n_links / 10, 0], high=self.n_links, size=2)
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if np.linalg.norm(self.goal) < self.n_links / 10:
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break
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qpos[-2:] = self.goal
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qvel = (
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# self.np_random.uniform(low=-0.005, high=0.005, size=self.model.nv) +
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self.init_qvel.copy()
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)
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qvel[-2:] = 0
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self.set_state(qpos, qvel)
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self._steps = 0
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return self._get_obs()
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def _get_obs(self):
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theta = self.sim.data.qpos.flat[:self.n_links]
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target = self.get_body_com("target")
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return np.concatenate([
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np.cos(theta),
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np.sin(theta),
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target[:2], # x-y of goal position
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self.sim.data.qvel.flat[:self.n_links], # angular velocity
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self.get_body_com("fingertip") - target, # goal distance
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])
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