update random rollout script

README.md

@@ -52,7 +52,7 @@ We are excited that this competition offers machine learning students, researche
 
 # How to run the environment
 
-To be added
+Install the environment from the [original nethack repository](https://github.com/facebookresearch/nle)
 
 # How to start participating
 
@@ -71,7 +71,7 @@ You can add your SSH Keys to your GitLab account by going to your profile settin
 3. **Install** competition specific dependencies!
     ```
     cd neurips-2021-nethack-starter-kit
-    pip3 install -r requirements.txt
+    pip install -r requirements.txt
     ```
 
 4. Try out random prediction codebase present in `test.py`.
@@ -136,7 +136,7 @@ To be added.
 
 ## Contributing
 
-🙏 You can share your solutions or any other baselines by contributing directly to this repository by opening merge request.
+You can share your solutions or any other baselines by contributing directly to this repository by opening merge request.
 
 - Add your implemntation as `test_<approach-name>.py`
 - Test it out using `python test_<approach-name>.py`
@@ -147,6 +147,8 @@ To be added.
 ## Contributors
 
 - [Shivam Khandelwal](https://www.aicrowd.com/participants/shivam)
+- [Jyotish Poonganam](https://www.aicrowd.com/participants/jyotish)
+- [Dipam chakraborty](https://www.aicrowd.com/participants/dipam)
 
 # 📎 Important links
 

docs/SUBMISSION.md

@@ -7,9 +7,7 @@ This file will help you in making your first submission.
 
 The evaluator will execute `run.sh` for generating predictions, so please remember to include it in your submission!
 
-The inline documentation of `test.py` will guide you with interfacing with the codebase properly. You can check TODOs inside it to learn about the functions you need to implement.
-
-You can modify the existing `test.py` OR copy it (to say `your_code.py`) and change it.
+You can modify the existing `rollout.py` OR copy it (to say `your_code.py`) and change it.
 
 
 ## IMPORTANT: Saving Models before submission!

requirements.txt

 aicrowd_api
+aicrowd_gym
 nle
+numpy
+gym

rollout.py

+#!/usr/bin/env python
+# This file is the entrypoint for your submission
+# You can modify this file to include your code or directly call your functions/modules from here.
+
+import aicrowd_gym
+import nle
+
+def main():
+    """
+    This function will be called for training phase.
+    """
+
+    # This allows us to limit the features of the environment 
+    # that we don't want participants to use during the submission
+    env = aicrowd_gym.make("NetHackScore-v0") 
+
+    env = aicrowd_gym.make("NetHackScore-v0")
+    env.reset()
+    done = False
+    episode_count = 0
+    while episode_count < 20:
+        _, _, done, _ = env.step(env.action_space.sample())
+        if done:
+            episode_count += 1
+            print(episode_count)
+            env.reset()
+
+if __name__ == "__main__":
+    main()

run.sh

 #!/bin/bash
-python predict.py
+python rollout.py
+

test.py

-#!/usr/bin/env python
-# This file is the entrypoint for your submission.
-# You can modify this file to include your code or directly call your functions/modules from here.
-
-
-def main():
-    """
-    This function will be called for training phase.
-    """
-
-    # Sample code for illustration, add your training code below
-    env = gym.make("NetHackScore-v0")
-
-#     actions = [env.action_space.sample() for _ in range(10)] # Just doing 10 samples in this example
-#     xposes = []
-#     for _ in range(1):
-#         obs = env.reset()
-#         done = False
-#         netr = 0
-
-#         # Limiting our code to 1024 steps in this example, you can do "while not done" to run till end
-#         while not done:
-
-            # To get better view in your training phase, it is suggested
-            # to register progress continuously, example when 54% completed
-            # aicrowd_helper.register_progress(0.54)
-
-    # Save trained model to train/ directory
-    # Training 100% Completed
-    aicrowd_helper.register_progress(1)
-    #env.close()
-
-
-if __name__ == "__main__":
-    main()

train.py

-#!/usr/bin/env python
-# This file is the entrypoint for your submission.
-# You can modify this file to include your code or directly call your functions/modules from here.
-
-
-def main():
-    """
-    This function will be called for training phase.
-    """
-
-    # Sample code for illustration, add your training code below
-    env = gym.make("NetHackScore-v0")
-
-#     actions = [env.action_space.sample() for _ in range(10)] # Just doing 10 samples in this example
-#     xposes = []
-#     for _ in range(1):
-#         obs = env.reset()
-#         done = False
-#         netr = 0
-
-#         # Limiting our code to 1024 steps in this example, you can do "while not done" to run till end
-#         while not done:
-
-            # To get better view in your training phase, it is suggested
-            # to register progress continuously, example when 54% completed
-            # aicrowd_helper.register_progress(0.54)
-
-    # Save trained model to train/ directory
-    # Training 100% Completed
-    aicrowd_helper.register_progress(1)
-    #env.close()
-
-
-if __name__ == "__main__":
-    main()