added asyc DMP example

alr_envs/utils/wrapper/mp_wrapper.py

@@ -1,4 +1,5 @@
 from abc import ABC, abstractmethod
+from collections import defaultdict
 
 import gym
 import numpy as np
@@ -49,24 +50,26 @@ class MPWrapper(gym.Wrapper, ABC):
         # self._velocity = velocity
 
         rewards = 0
-        infos = []
+        # infos = defaultdict(list)
 
         # TODO: @Max Why do we need this configure, states should be part of the model
         # self.env.configure(context)
         obs = self.env.reset()
+        info = {}
 
         for t, pos_vel in enumerate(zip(trajectory, velocity)):
             ac = self.policy.get_action(pos_vel[0], pos_vel[1])
             obs, rew, done, info = self.env.step(ac)
             rewards += rew
-            infos.append(info)
+            # TODO return all dicts?
+            # [infos[k].append(v) for k, v in info.items()]
             if self.render_mode:
                 self.env.render(mode=self.render_mode, **self.render_kwargs)
             if done:
                 break
 
         done = True
-        return obs, rewards, done, infos
+        return obs, rewards, done, info
 
     def render(self, mode='human', **kwargs):
         """Only set render options here, such that they can be used during the rollout.

example.py

@@ -1,4 +1,7 @@
+from collections import defaultdict
+
 import gym
+import numpy as np
 
 
 def example_mujoco():
@@ -36,7 +39,7 @@ def example_dmp():
             # render full DMP trajectory
             # render can only be called once in the beginning as well. That would render every trajectory
             # Calling it after every trajectory allows to modify the mode. mode=None, disables rendering.
-            env.render(mode="partial")
+            env.render(mode="human")
 
         if done:
             print(rewards)
@@ -44,5 +47,40 @@ def example_dmp():
             obs = env.reset()
 
 
+def example_async(n_cpu=4, seed=int('533D', 16)):
+    def make_env(env_id, seed, rank):
+        env = gym.make(env_id)
+        env.seed(seed + rank)
+        return lambda: env
+
+    def sample(env: gym.vector.VectorEnv, n_samples=100):
+        # for plotting
+        rewards = np.zeros(n_cpu)
+
+        # this would generate more samples than requested if n_samples % num_envs != 0
+        repeat = int(np.ceil(n_samples / env.num_envs))
+        vals = defaultdict(list)
+        for i in range(repeat):
+            obs, reward, done, info = envs.step(envs.action_space.sample())
+            vals['obs'].append(obs)
+            vals['reward'].append(reward)
+            vals['done'].append(done)
+            vals['info'].append(info)
+            rewards += reward
+            if np.any(done):
+                print(rewards[done])
+                rewards[done] = 0
+
+        # do not return values above threshold
+        return (*map(lambda v: np.stack(v)[:n_samples], vals.values()),)
+
+    envs = gym.vector.AsyncVectorEnv([make_env("alr_envs:HoleReacherDMP-v0", seed, i) for i in range(n_cpu)])
+
+    obs = envs.reset()
+    print(sample(envs, 16))
+
+
 if __name__ == '__main__':
-    example_dmp()
+    # example_mujoco()
+    # example_dmp()
+    example_async()