1import gymnasium as gym
2import fancy_gym
3
4
5def example_dmc(env_id="dm_control/fish-swim", seed=1, iterations=1000, render=True):
6 """
7 Example for running a DMC based env in the step based setting.
8 The env_id has to be specified as `domain_name:task_name` or
9 for manipulation tasks as `domain_name:manipulation-environment_name`
10
11 Args:
12 env_id: Either `domain_name-task_name` or `manipulation-environment_name`
13 seed: seed for deterministic behaviour
14 iterations: Number of rollout steps to run
15 render: Render the episode
16
17 Returns:
18
19 """
20 env = gym.make(env_id, render_mode='human' if render else None)
21 rewards = 0
22 obs = env.reset(seed=seed)
23 print("observation shape:", env.observation_space.shape)
24 print("action shape:", env.action_space.shape)
25
26 for i in range(iterations):
27 ac = env.action_space.sample()
28 if render:
29 env.render()
30 obs, reward, terminated, truncated, info = env.step(ac)
31 rewards += reward
32
33 if terminated or truncated:
34 print(env_id, rewards)
35 rewards = 0
36 obs = env.reset()
37
38 env.close()
39 del env
40
41
42def example_custom_dmc_and_mp(seed=1, iterations=1, render=True):
43 """
44 Example for running a custom movement primitive based environments.
45 Our already registered environments follow the same structure.
46 Hence, this also allows to adjust hyperparameters of the movement primitives.
47 Yet, we recommend the method above if you are just interested in chaining those parameters for existing tasks.
48 We appreciate PRs for custom environments (especially MP wrappers of existing tasks)
49 for our repo: https://github.com/ALRhub/fancy_gym/
50 Args:
51 seed: seed for deterministic behaviour
52 iterations: Number of rollout steps to run
53 render: Render the episode
54
55 Returns:
56
57 """
58
59 # Base DMC name, according to structure of above example
60 base_env_id = "dm_control/ball_in_cup-catch"
61
62 # Replace this wrapper with the custom wrapper for your environment by inheriting from the RawInterfaceWrapper.
63 # You can also add other gym.Wrappers in case they are needed.
64 wrappers = [fancy_gym.dmc.suite.ball_in_cup.MPWrapper]
65 # # For a ProMP
66 trajectory_generator_kwargs = {'trajectory_generator_type': 'promp'}
67 phase_generator_kwargs = {'phase_generator_type': 'linear'}
68 controller_kwargs = {'controller_type': 'motor',
69 "p_gains": 1.0,
70 "d_gains": 0.1, }
71 basis_generator_kwargs = {'basis_generator_type': 'zero_rbf',
72 'num_basis': 5,
73 'num_basis_zero_start': 1
74 }
75
76 # For a DMP
77 # trajectory_generator_kwargs = {'trajectory_generator_type': 'dmp'}
78 # phase_generator_kwargs = {'phase_generator_type': 'exp',
79 # 'alpha_phase': 2}
80 # controller_kwargs = {'controller_type': 'motor',
81 # "p_gains": 1.0,
82 # "d_gains": 0.1,
83 # }
84 # basis_generator_kwargs = {'basis_generator_type': 'rbf',
85 # 'num_basis': 5
86 # }
87 base_env = gym.make(base_env_id, render_mode='human' if render else None)
88 env = fancy_gym.make_bb(env=base_env, wrappers=wrappers, black_box_kwargs={},
89 traj_gen_kwargs=trajectory_generator_kwargs, controller_kwargs=controller_kwargs,
90 phase_kwargs=phase_generator_kwargs, basis_kwargs=basis_generator_kwargs,
91 seed=seed)
92
93 # This renders the full MP trajectory
94 # It is only required to call render() once in the beginning, which renders every consecutive trajectory.
95 # Resetting to no rendering, can be achieved by render(mode=None).
96 # It is also possible to change them mode multiple times when
97 # e.g. only every nth trajectory should be displayed.
98 if render:
99 env.render()
100
101 rewards = 0
102 obs = env.reset()
103
104 # number of samples/full trajectories (multiple environment steps)
105 for i in range(iterations):
106 ac = env.action_space.sample()
107 obs, reward, terminated, truncated, info = env.step(ac)
108 rewards += reward
109
110 if terminated or truncated:
111 print(base_env_id, rewards)
112 rewards = 0
113 obs = env.reset()
114
115 env.close()
116 del env
117
118def main(render = False):
119 # # Standard DMC Suite tasks
120 example_dmc("dm_control/fish-swim", seed=10, iterations=1000, render=render)
121 #
122 # # Manipulation tasks
123 # # Disclaimer: The vision versions are currently not integrated and yield an error
124 example_dmc("dm_control/reach_site_features", seed=10, iterations=250, render=render)
125 #
126 # # Gym + DMC hybrid task provided in the MP framework
127 example_dmc("dm_control_ProMP/ball_in_cup-catch-v0", seed=10, iterations=1, render=render)
128
129 # Custom DMC task # Different seed, because the episode is longer for this example and the name+seed combo is
130 # already registered above
131 example_custom_dmc_and_mp(seed=11, iterations=1, render=render)
132
133 # # Standard DMC Suite tasks
134 example_dmc("dm_control/fish-swim", seed=10, iterations=1000, render=render)
135 #
136 # # Manipulation tasks
137 # # Disclaimer: The vision versions are currently not integrated and yield an error
138 example_dmc("dm_control/reach_site_features", seed=10, iterations=250, render=render)
139 #
140 # # Gym + DMC hybrid task provided in the MP framework
141 example_dmc("dm_control_ProMP/ball_in_cup-catch-v0", seed=10, iterations=1, render=render)
142
143 # Custom DMC task # Different seed, because the episode is longer for this example and the name+seed combo is
144 # already registered above
145 example_custom_dmc_and_mp(seed=11, iterations=1, render=render)
146
147if __name__ == '__main__':
148 main()