Issue template

.github/ISSUE_TEMPLATE/bug.yml

@@ -0,0 +1,48 @@
+name: Bug Report
+description: Submit a bug report
+title: "[Bug Report] Bug title"
+labels: ["bug"]
+body:
+  - type: textarea
+    id: description
+    attributes:
+      label: Describe the bug
+      description: A clear and concise description of what the bug is.
+    validations:
+      required: true
+
+  - type: textarea
+    id: code-example
+    attributes:
+      label: Code example
+      description: |
+        Please try to provide a minimal example to reproduce the bug. Error messages and stack traces are also helpful.
+        This will be automatically formatted into code, so no need for backticks.
+      render: shell
+
+  - type: textarea
+    id: system-info
+    attributes:
+      label: System info
+      description: |
+        Describe the characteristic of your environment:
+        * Describe how Gymnasium was installed (pip, docker, source, ...)
+        * Version of `gymnasium` (by `gymnasium.__version__`)
+        * What OS/version of Linux you're using. Note that while we will accept PRs to improve Window's support, we do not officially support it.
+        * Python version
+
+  - type: textarea
+    id: additional-context
+    attributes:
+      label: Additional context
+      description: Add any other context about the problem here.
+
+  - type: checkboxes
+    id: checklist
+    attributes:
+      label: Checklist
+      options:
+        - label: >
+            I have checked that there is no similar [issue](https://github.com/Farama-Foundation/Gymnasium/issues) in
+            the repo
+          required: true

.github/ISSUE_TEMPLATE/proposal.yml

@@ -0,0 +1,49 @@
+name: Proposal
+description: Propose changes that are not fixing bugs
+title: "[Proposal] Proposal title"
+labels: ["enhancement"]
+body:
+  - type: textarea
+    id: proposal
+    attributes:
+      label: Proposal
+      description: A clear and concise description of the proposal.
+    validations:
+      required: true
+
+  - type: textarea
+    id: motivation
+    attributes:
+      label: Motivation
+      description: |
+        Please outline the motivation for the proposal.
+        Is your feature request related to a problem? e.g.,"I'm always frustrated when [...]".
+        If this is related to another GitHub issue, please link here too.
+
+  - type: textarea
+    id: pitch
+    attributes:
+      label: Pitch
+      description: A clear and concise description of what you want to happen.
+
+  - type: textarea
+    id: alternatives
+    attributes:
+      label: Alternatives
+      description: A clear and concise description of any alternative solutions or features you've considered, if any.
+
+  - type: textarea
+    id: additional-context
+    attributes:
+      label: Additional context
+      description: Add any other context or screenshots about the feature request here.
+
+  - type: checkboxes
+    id: checklist
+    attributes:
+      label: Checklist
+      options:
+        - label: >
+            I have checked that there is no similar [issue](https://github.com/Farama-Foundation/Gymnasium/issues) in
+            the repo
+          required: true

.github/ISSUE_TEMPLATE/question.yml

@@ -0,0 +1,21 @@
+name: Question
+description: Ask a question
+title: "[Question] Question title"
+labels: ["question"]
+body:
+  - type: markdown
+    attributes:
+      value: >
+        If you're a beginner and have basic questions, please ask on
+        [r/reinforcementlearning](https://www.reddit.com/r/reinforcementlearning/) or in the
+        [RL Discord](https://discord.com/invite/xhfNqQv) (if you're new please use the beginners channel).
+        Basic questions that are not bugs or feature requests will be closed without reply, because GitHub
+        issues are not an appropriate venue for these. Advanced/nontrivial questions, especially in areas where
+        documentation is lacking, are very much welcome.
+  - type: textarea
+    id: question
+    attributes:
+      label: Question
+      description: Your question
+    validations:
+      required: true

README.md

@@ -76,12 +76,12 @@ You may wish to only access individual environments used in the Metaworld benchm
 
 ### Seeding a Benchmark Instance
 For the purposes of reproducibility, it may be important to you to seed your benchmark instance.
-For example, for the ML1 benchmark environment with the 'pick-place-v2' environment, you can do so in the following way:
+For example, for the ML1 benchmark environment with the 'pick-place-V3' environment, you can do so in the following way:
 ```python
 import metaworld
 
 SEED = 0  # some seed number here
-benchmark = metaworld.ML1('pick-place-v2', seed=SEED)
+benchmark = metaworld.ML1('pick-place-V3', seed=SEED)
 ```
 
 ### Running ML1 or MT1
@@ -91,9 +91,9 @@ import random
 
 print(metaworld.ML1.ENV_NAMES)  # Check out the available environments
 
-ml1 = metaworld.ML1('pick-place-v2') # Construct the benchmark, sampling tasks
+ml1 = metaworld.ML1('pick-place-V3') # Construct the benchmark, sampling tasks
 
-env = ml1.train_classes['pick-place-v2']()  # Create an environment with task `pick_place`
+env = ml1.train_classes['pick-place-V3']()  # Create an environment with task `pick_place`
 task = random.choice(ml1.train_tasks)
 env.set_task(task)  # Set task
 
@@ -152,15 +152,15 @@ environments respectively.
 
 You can access them in the following way:
 ```python
-from metaworld.envs import (ALL_V2_ENVIRONMENTS_GOAL_OBSERVABLE,
-                            ALL_V2_ENVIRONMENTS_GOAL_HIDDEN)
+from metaworld.envs import (ALL_V3_ENVIRONMENTS_GOAL_OBSERVABLE,
+                            ALL_V3_ENVIRONMENTS_GOAL_HIDDEN)
                             # these are ordered dicts where the key : value
                             # is env_name : env_constructor
 
 import numpy as np
 
-door_open_goal_observable_cls = ALL_V2_ENVIRONMENTS_GOAL_OBSERVABLE["door-open-v2-goal-observable"]
-door_open_goal_hidden_cls = ALL_V2_ENVIRONMENTS_GOAL_HIDDEN["door-open-v2-goal-hidden"]
+door_open_goal_observable_cls = ALL_V3_ENVIRONMENTS_GOAL_OBSERVABLE["door-open-V3-goal-observable"]
+door_open_goal_hidden_cls = ALL_V3_ENVIRONMENTS_GOAL_HIDDEN["door-open-V3-goal-hidden"]
 
 env = door_open_goal_hidden_cls()
 env.reset()  # Reset environment
@@ -171,15 +171,15 @@ assert (obs[-3:] == np.zeros(3)).all() # goal will be zeroed out because env is
 # You can choose to initialize the random seed of the environment.
 # The state of your rng will remain unaffected after the environment is constructed.
 env1 = door_open_goal_observable_cls(seed=5)
-env2 = door_open_goal_observable_cls(seed=5)
+enV3 = door_open_goal_observable_cls(seed=5)
 
 env1.reset()  # Reset environment
-env2.reset()
+enV3.reset()
 a1 = env1.action_space.sample()  # Sample an action
-a2 = env2.action_space.sample()
+a2 = enV3.action_space.sample()
 next_obs1, _, _, _ = env1.step(a1)  # Step the environment with the sampled random action
 
-next_obs2, _, _, _ = env2.step(a2)
+next_obs2, _, _, _ = enV3.step(a2)
 assert (next_obs1[-3:] == next_obs2[-3:]).all() # 2 envs initialized with the same seed will have the same goal
 assert not (next_obs2[-3:] == np.zeros(3)).all()   # The env's are goal observable, meaning the goal is not zero'd out
 

docker/Dockerfile

@@ -16,7 +16,7 @@ COPY . /usr/local/metaworld/
 WORKDIR /usr/local/metaworld/
 RUN free -g
 RUN pip install .[testing]
-RUN git clone https://github.com/reginald-mclean/Gymnasium.git
+RUN git clone https://github.com/Farama-Foundation/Gymnasium.git
 RUN pip install -e Gymnasium
 
 

docs/index.md

@@ -25,9 +25,9 @@ import random
 
 print(metaworld.ML1.ENV_NAMES)  # Check out the available environments
 
-ml1 = metaworld.ML1('pick-place-v2') # Construct the benchmark, sampling tasks
+ml1 = metaworld.ML1('pick-place-V3') # Construct the benchmark, sampling tasks
 
-env = ml1.train_classes['pick-place-v2']()  # Create an environment with task `pick_place`
+env = ml1.train_classes['pick-place-V3']()  # Create an environment with task `pick_place`
 task = random.choice(ml1.train_tasks)
 env.set_task(task)  # Set task
 
@@ -41,6 +41,7 @@ obs, reward, terminate, truncate, info = env.step(a)
 :caption: Introduction
 
 introduction/basic_usage
+evaluation/evaluation
 installation/installation
 rendering/rendering
 usage/basic_usage