Upload of a new agent trained with Remote-DQN on Taxi-v3

README.md

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+---
+tags:
+- Taxi-v3
+- reinforcement-learning
+- rl-framework
+model-index:
+- name: Remote-DQN-Taxi-v3
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: Taxi-v3
+      type: Taxi-v3
+    metrics:
+    - type: mean_reward
+      value: -200.00 +/- 0.00
+      name: mean_reward
+      verified: false
+---
+
+
+# Custom implemented Remote-DQN agent playing on *Taxi-v3*
+
+This is a trained model of an agent playing on the environment *Taxi-v3*.
+The agent was trained with a Remote-DQN algorithm and evaluated for 50 episodes.
+See further agent and evaluation metadata in the according README section.
+
+
+## Import
+The Python module used for training and uploading/downloading is [rl-framework](https://github.com/alexander-zap/rl-framework).
+It is an easy-to-read, plug-and-use Reinforcement Learning framework and provides standardized interfaces
+and implementations to various Reinforcement Learning methods and environments.
+
+Also it provides connectors for the upload and download to popular model version control systems,
+including the HuggingFace Hub.
+
+## Usage
+```python
+
+from rl-framework import StableBaselinesAgent, StableBaselinesAlgorithm
+
+# Create new agent instance
+agent = StableBaselinesAgent(
+    algorithm=StableBaselinesAlgorithm.Remote-DQN
+    algorithm_parameters={
+        ...
+    },
+)
+
+# Download existing agent from HF Hub
+repository_id = "zap-thamm/Remote-DQN-Taxi-v3"
+file_name = "algorithm.zip"
+agent.download(repository_id=repository_id, filename=file_name)
+
+```
+
+Further examples can be found in the [exploration section of the rl-framework repository](https://github.com/alexander-zap/rl-framework/tree/main/exploration).
+
+        

algorithm.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1f3c2bfa5151a43dad3d4e3daadcea481a7d48ece46992e245b7d99443e30459
+size 612148

results.json

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+{"env_id": "Taxi-v3", "mean_reward": -200.0, "n_eval_episodes": 50, "eval_datetime": "2023-12-28T01:06:36.190507"}

system.json

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+{"OS": "Windows-10-10.0.19045-SP0 10.0.19045", "Python": "3.10.8", "Stable-Baselines3": "2.2.1", "PyTorch": "2.1.2+cpu", "GPU Enabled": "False", "Numpy": "1.26.2", "Cloudpickle": "3.0.0", "Gymnasium": "0.29.1"}