updating env.step return term/trunc

docs/index.md

@@ -33,7 +33,7 @@ env.set_task(task)  # Set task
 
 obs = env.reset()  # Reset environment
 a = env.action_space.sample()  # Sample an action
-obs, reward, done, info = env.step(a)
+obs, reward, terminate, truncate, info = env.step(a)
 ```
 
 ```{toctree}

docs/introduction/basic_usage.md

@@ -51,7 +51,7 @@ env.set_task(task)  # Set task
 
 obs = env.reset()  # Reset environment
 a = env.action_space.sample()  # Sample an action
-obs, reward, done, info = env.step(a)  # Step the environment with the sampled random action
+obs, reward, terminate, truncate, info = env.step(a)  # Step the environment with the sampled random action
 ```
 __MT1__ can be run the same way except that it does not contain any `test_tasks`
 
@@ -75,7 +75,7 @@ for name, env_cls in ml10.train_classes.items():
 for env in training_envs:
   obs = env.reset()  # Reset environment
   a = env.action_space.sample()  # Sample an action
-  obs, reward, done, info = env.step(a)  # Step the environment with the sampled random action
+  obs, reward, terminate, truncate, info = env.step(a)  # Step the environment with the sampled random action
 ```
 Create an environment with test tasks (this only works for ML10 and ML45, since MT10 and MT50 don't have a separate set of test tasks):
 ```python
@@ -95,7 +95,7 @@ for name, env_cls in ml10.test_classes.items():
 for env in testing_envs:
   obs = env.reset()  # Reset environment
   a = env.action_space.sample()  # Sample an action
-  obs, reward, done, info = env.step(a)  # Step the environment with the sampled random action
+  obs, reward, terminate, truncate, info = env.step(a)  # Step the environment with the sampled random action
 ```
 
 ## Accessing Single Goal Environments
@@ -119,7 +119,7 @@ door_open_goal_hidden_cls = ALL_V2_ENVIRONMENTS_GOAL_HIDDEN["door-open-v2-goal-h
 env = door_open_goal_hidden_cls()
 env.reset()  # Reset environment
 a = env.action_space.sample()  # Sample an action
-obs, reward, done, info = env.step(a)  # Step the environment with the sampled random action
+obs, reward, terminate, truncate, info = env.step(a)  # Step the environment with the sampled random action
 assert (obs[-3:] == np.zeros(3)).all() # goal will be zeroed out because env is HiddenGoal
 
 # You can choose to initialize the random seed of the environment.
@@ -131,7 +131,7 @@ env1.reset()  # Reset environment
 env2.reset()
 a1 = env1.action_space.sample()  # Sample an action
 a2 = env2.action_space.sample()
-next_obs1, _, _, _ = env1.step(a1)  # Step the environment with the sampled random action
+next_obs1, _, _, _, _ = env1.step(a1)  # Step the environment with the sampled random action
 
 next_obs2, _, _, _ = env2.step(a2)
 assert (next_obs1[-3:] == next_obs2[-3:]).all() # 2 envs initialized with the same seed will have the same goal
@@ -142,8 +142,8 @@ env1.reset()  # Reset environment
 env3.reset()
 a1 = env1.action_space.sample()  # Sample an action
 a3 = env3.action_space.sample()
-next_obs1, _, _, _ = env1.step(a1)  # Step the environment with the sampled random action
-next_obs3, _, _, _ = env3.step(a3)
+next_obs1, _, _, _, _ = env1.step(a1)  # Step the environment with the sampled random action
+next_obs3, _, _, _, _ = env3.step(a3)
 
 assert not (next_obs1[-3:] == next_obs3[-3:]).all() # 2 envs initialized with different seeds will have different goals
 assert not (next_obs1[-3:] == np.zeros(3)).all()   # The env's are goal observable, meaning the goal is not zero'd out

docs/rendering/rendering.md

@@ -1 +1,51 @@
 # Rendering
+
+Each Meta-World environment uses Gymnasium to handle the rendering functions following the [`gymnasium.MujocoEnv`](https://github.com/Farama-Foundation/Gymnasium/blob/94a7909042e846c496bcf54f375a5d0963da2b31/gymnasium/envs/mujoco/mujoco_env.py#L184) interface.
+
+['https://gymnasium.farama.org/api/env/#gymnasium.Env.render']#Gymnasium. 
+
+Upon environment creation a user can select a render mode in ('rgb_array', 'human').
+
+For example:
+
+```python
+import metaworld
+import random
+
+print(metaworld.ML1.ENV_NAMES)  # Check out the available environments
+
+env_name = '' # Pick an environment name
+
+render_mode = '' # set a render mode
+
+ml1 = metaworld.ML1(env_name) # Construct the benchmark, sampling tasks
+
+env = ml1.train_classes[env_name](render_mode=render_mode)  
+task = random.choice(ml1.train_tasks)
+env.set_task(task)  # Set task
+
+obs = env.reset()  # Reset environment
+a = env.action_space.sample()  # Sample an action
+obs, reward, done, info = env.step(a)  # Step the environment with the sampled random action
+```
+
+# Render from specific camera
+
+In addition to the base render functions, Meta-World supports multiple camera positions.
+
+```python
+camera_name = '' # one of: ['corner', 'corner2', 'corner3', 'topview', 'behindGripper', 'gripperPOV']
+
+env = ml1.train_classes[env_name](render_mode=render_mode, camera_name=camera_name)
+
+```
+
+The ID of the camera (from Mujoco) can also be passed if known.
+
+```python
+
+camera_id = '' # this is an integer that represents the camera ID from Mujoco
+
+env = ml1.train_classes[env_name](render_mode=render_mode, camera_id=camera_id)
+
+```