updated for new mp_pytorch

alr_envs/alr/mujoco/reacher/alr_reacher.py

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

alr_envs/alr/mujoco/reacher/new_mp_wrapper.py

@@ -1,24 +0,0 @@
-from alr_envs.mp.episodic_wrapper import EpisodicWrapper
-from typing import Union, Tuple
-import numpy as np
-
-
-class NewMPWrapper(EpisodicWrapper):
-
-    @property
-    def current_pos(self) -> Union[float, int, np.ndarray, Tuple]:
-        return self.env.sim.data.qpos.flat[:self.env.n_links]
-    @property
-    def current_vel(self) -> Union[float, int, np.ndarray, Tuple]:
-        return self.env.sim.data.qvel.flat[:self.env.n_links]
-
-    def set_active_obs(self):
-        return np.concatenate([
-            [False] * self.env.n_links,  # cos
-            [False] * self.env.n_links,  # sin
-            [True] * 2,  # goal position
-            [False] * self.env.n_links,  # angular velocity
-            [False] * 3,  # goal distance
-            # self.get_body_com("target"),  # only return target to make problem harder
-            [False],  # step
-        ])

fancy_gym/black_box/black_box_wrapper.py

@@ -86,7 +86,7 @@ class BlackBoxWrapper(gym.ObservationWrapper):
         bc_time = np.array(0 if not self.do_replanning else self.current_traj_steps * self.dt)
         # TODO we could think about initializing with the previous desired value in order to have a smooth transition
         #  at least from the planning point of view.
-        self.traj_gen.set_boundary_conditions(bc_time, self.current_pos, self.current_vel)
+        self.traj_gen.set_initial_conditions(bc_time, self.current_pos, self.current_vel)
         self.traj_gen.set_duration(duration, self.dt)
         # traj_dict = self.traj_gen.get_trajs(get_pos=True, get_vel=True)
         position = get_numpy(self.traj_gen.get_traj_pos())

fancy_gym/envs/classic_control/simple_reacher/__init__.py

@@ -1,2 +1 @@
 from .mp_wrapper import MPWrapper
-from .new_mp_wrapper import NewMPWrapper

fancy_gym/meta/__init__.py

@@ -36,7 +36,8 @@ DEFAULT_BB_DICT_ProDMP = {
         'trajectory_generator_type': 'prodmp',
         'auto_scale_basis': True,
         'weights_scale': 10,
-        'goal_scale': 0.
+        # 'goal_scale': 0.,
+        'disable_goal': True,
     },
     "phase_generator_kwargs": {
         'phase_generator_type': 'exp',