working pd gain tuning example

alr_envs/examples/examples_movement_primitives.py

@@ -157,10 +157,10 @@ def example_fully_custom_mp(seed=1, iterations=1, render=True):
 if __name__ == '__main__':
     render = True
     # DMP
-    example_mp("alr_envs:HoleReacherDMP-v0", seed=10, iterations=5, render=render)
+    example_mp("HoleReacherDMP-v0", seed=10, iterations=5, render=render)
     #
     # # ProMP
-    example_mp("alr_envs:HoleReacherProMP-v0", seed=10, iterations=5, render=render)
+    example_mp("HoleReacherProMP-v0", seed=10, iterations=5, render=render)
 
     # Altered basis functions
     obs1 = example_custom_mp("Reacher5dProMP-v0", seed=10, iterations=5, render=render)

alr_envs/examples/pd_control_gain_tuning.py

@@ -1,55 +1,24 @@
 from collections import OrderedDict
 
 import numpy as np
+import alr_envs
 from matplotlib import pyplot as plt
 
-from alr_envs import make_bb, dmc, meta
-from alr_envs.envs import mujoco
-
-
-def visualize(env):
-    t = env.t
-    pos_features = env.traj_gen.basis_generator.basis(t)
-    plt.plot(t, pos_features)
-    plt.show()
-
-
 # This might work for some environments, however, please verify either way the correct trajectory information
 # for your environment are extracted below
 SEED = 1
-# env_id = "dmc:ball_in_cup-catch"
-# wrappers = [dmc.suite.ball_in_cup.MPWrapper]
-env_id = "Reacher5dSparse-v0"
-wrappers = [mujoco.reacher.MPWrapper]
-# env_id = "metaworld:button-press-v2"
-# wrappers = [meta.goal_object_change_mp_wrapper.MPWrapper]
 
-mp_kwargs = {
+env_id = "Reacher5dProMP-v0"
-    "num_dof": 4,
-    "num_basis": 5,
-    "duration": 6.25,
-    "policy_type": "metaworld",
-    "weights_scale": 10,
-    "zero_start": True,
-    # "policy_kwargs": {
-    #     "p_gains": 1,
-    #     "d_gains": 0.1
-    # }
-}
 
-# kwargs = dict(time_limit=4, episode_length=200)
+env = alr_envs.make(env_id, seed=SEED, controller_kwargs={'p_gains': 0.05, 'd_gains': 0.05}).env
-kwargs = {}
-
-env = make_bb(env_id, wrappers, seed=SEED, mp_kwargs=mp_kwargs, **kwargs)
 env.action_space.seed(SEED)
 
 # Plot difference between real trajectory and target MP trajectory
 env.reset()
-w = env.action_space.sample()  # N(0,1)
+w = env.action_space.sample()
-visualize(env)
+pos, vel = env.get_trajectory(w)
-pos, vel = env.mp_rollout(w)
 
-base_shape = env.full_action_space.shape
+base_shape = env.env.action_space.shape
 actual_pos = np.zeros((len(pos), *base_shape))
 actual_vel = np.zeros((len(pos), *base_shape))
 act = np.zeros((len(pos), *base_shape))
@@ -57,31 +26,30 @@ act = np.zeros((len(pos), *base_shape))
 plt.ion()
 fig = plt.figure()
 ax = fig.add_subplot(1, 1, 1)
-img = ax.imshow(env.env.render("rgb_array"))
+
+img = ax.imshow(env.env.render(mode="rgb_array"))
 fig.show()
 
 for t, pos_vel in enumerate(zip(pos, vel)):
-    actions = env.policy.get_action(pos_vel[0], pos_vel[1], env.current_vel, env.current_pos)
+    actions = env.tracking_controller.get_action(pos_vel[0], pos_vel[1], env.current_vel, env.current_pos)
-    actions = np.clip(actions, env.full_action_space.low, env.full_action_space.high)
+    actions = np.clip(actions, env.env.action_space.low, env.env.action_space.high)
     _, _, _, _ = env.env.step(actions)
     if t % 15 == 0:
-        img.set_data(env.env.render("rgb_array"))
+        img.set_data(env.env.render(mode="rgb_array"))
         fig.canvas.draw()
         fig.canvas.flush_events()
     act[t, :] = actions
     # TODO verify for your environment
     actual_pos[t, :] = env.current_pos
-    actual_vel[t, :] = 0  # env.current_vel
+    actual_vel[t, :] = env.current_vel
 
 plt.figure(figsize=(15, 5))
 
 plt.subplot(131)
 plt.title("Position")
 p1 = plt.plot(actual_pos, c='C0', label="true")
-# plt.plot(actual_pos_ball, label="true pos ball")
+p2 = plt.plot(pos, c='C1', label="MP")
-p2 = plt.plot(pos, c='C1', label="MP")  # , label=["MP" if i == 0 else None for i in range(np.prod(base_shape))])
 plt.xlabel("Episode steps")
-# plt.legend()
 handles, labels = plt.gca().get_legend_handles_labels()
 
 by_label = OrderedDict(zip(labels, handles))
@@ -95,7 +63,6 @@ plt.xlabel("Episode steps")
 
 plt.subplot(133)
 plt.title(f"Actions {np.std(act, axis=0)}")
-plt.plot(act, c="C0"),  # label=[f"actions" if i == 0 else "" for i in range(np.prod(base_action_shape))])
+plt.plot(act, c="C0"),
 plt.xlabel("Episode steps")
-# plt.legend()
 plt.show()