first updats to reacher and env creation

fancy_gym/envs/mujoco/reacher/reacher.py

@@ -3,6 +3,7 @@ import os
 import numpy as np
 from gym import utils
 from gym.envs.mujoco import MujocoEnv
+from gym.spaces import Box
 
 MAX_EPISODE_STEPS_REACHER = 200
 
@@ -12,7 +13,17 @@ class ReacherEnv(MujocoEnv, utils.EzPickle):
     More general version of the gym mujoco Reacher environment
     """
 
-    def __init__(self, sparse: bool = False, n_links: int = 5, reward_weight: float = 1, ctrl_cost_weight: float = 1):
+    metadata = {
+        "render_modes": [
+            "human",
+            "rgb_array",
+            "depth_array",
+        ],
+        "render_fps": 50,
+    }
+
+    def __init__(self, sparse: bool = False, n_links: int = 5, reward_weight: float = 1, ctrl_cost_weight: float = 1.,
+                 **kwargs):
         utils.EzPickle.__init__(**locals())
 
         self._steps = 0
@@ -25,10 +36,16 @@ class ReacherEnv(MujocoEnv, utils.EzPickle):
 
         file_name = f'reacher_{n_links}links.xml'
 
+        # sin, cos, velocity * n_Links + goal position (2) and goal distance (3)
+        shape = (self.n_links * 3 + 5,)
+        observation_space = Box(low=-np.inf, high=np.inf, shape=shape, dtype=np.float64)
+
         MujocoEnv.__init__(self,
                            model_path=os.path.join(os.path.dirname(__file__), "assets", file_name),
                            frame_skip=2,
-                           mujoco_bindings="mujoco")
+                           observation_space=observation_space,
+                           **kwargs
+                           )
 
     def step(self, action):
         self._steps += 1
@@ -45,10 +62,14 @@ class ReacherEnv(MujocoEnv, utils.EzPickle):
 
         reward = reward_dist + reward_ctrl + angular_vel
         self.do_simulation(action, self.frame_skip)
-        ob = self._get_obs()
+        if self.render_mode == "human":
-        done = False
+            self.render()
 
-        infos = dict(
+        ob = self._get_obs()
+        terminated = False
+        truncated = False
+
+        info = dict(
             reward_dist=reward_dist,
             reward_ctrl=reward_ctrl,
             velocity=angular_vel,
@@ -56,7 +77,7 @@ class ReacherEnv(MujocoEnv, utils.EzPickle):
             goal=self.goal if hasattr(self, "goal") else None
         )
 
-        return ob, reward, done, infos
+        return ob, reward, terminated, truncated, info
 
     def distance_reward(self):
         vec = self.get_body_com("fingertip") - self.get_body_com("target")
@@ -66,6 +87,7 @@ class ReacherEnv(MujocoEnv, utils.EzPickle):
         return -10 * np.square(self.data.qvel.flat[:self.n_links]).sum() if self.sparse else 0.0
 
     def viewer_setup(self):
+        assert self.viewer is not None
         self.viewer.cam.trackbodyid = 0
 
     def reset_model(self):

fancy_gym/utils/make_env_helpers.py

@@ -9,6 +9,7 @@ from typing import Iterable, Type, Union
 import gym
 import numpy as np
 from gym.envs.registration import register, registry
+from gym.utils import seeding
 
 try:
     from dm_control import suite, manipulation
@@ -88,7 +89,9 @@ def make(env_id: str, seed: int, **kwargs):
     else:
         env = make_gym(env_id, seed, **kwargs)
 
-    env.seed(seed)
+    np_random, _ = seeding.np_random(seed)
+    env.np_random = np_random
+    # env.seed(seed)
     env.action_space.seed(seed)
     env.observation_space.seed(seed)
 

test/test_fancy_envs.py

@@ -6,7 +6,7 @@ import pytest
 
 from test.utils import run_env, run_env_determinism
 
-CUSTOM_IDS = [spec.id for spec in gym.envs.registry.all() if
+CUSTOM_IDS = [id for id, spec in gym.envs.registry.items() if
               "fancy_gym" in spec.entry_point and 'make_bb_env_helper' not in spec.entry_point]
 CUSTOM_MP_IDS = itertools.chain(*fancy_gym.ALL_FANCY_MOVEMENT_PRIMITIVE_ENVIRONMENTS.values())
 SEED = 1

test/utils.py

@@ -24,6 +24,7 @@ def run_env(env_id, iterations=None, seed=0, render=False):
     actions = []
     dones = []
     obs = env.reset()
+    print(obs.dtype)
     verify_observations(obs, env.observation_space, "reset()")
 
     iterations = iterations or (env.spec.max_episode_steps or 1)