alr_reacher task

alr_envs/alr/mujoco/reacher/alr_reacher.py

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+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=200, n_links=5, balance=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.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
+        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)
+            angular_vel -= np.linalg.norm(self.sim.data.qvel.flat[:self.n_links])
+        reward_ctrl = - np.square(a).sum()
+
+        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.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]
+        return np.concatenate([
+            np.cos(theta),
+            np.sin(theta),
+            self.sim.data.qpos.flat[self.n_links:],  # this is goal position
+            self.sim.data.qvel.flat[:self.n_links],  # this is angular velocity
+            self.get_body_com("fingertip") - self.get_body_com("target"),
+            # self.get_body_com("target"),  # only return target to make problem harder
+            [self._steps],
+        ])