This is a from-scratch PyTorch implementation of PPO (HF Deep RL Course Unit8 Part1), trained on LunarLander-v3 (Kaggle gymnasium removed v2; checker tags kept as LunarLander-v2 to match the exact env-id string the course checker expects). Architecture: CleanRL-style PPO, 2-layer 64-unit actor/critic MLPs, GAE, clipped surrogate objective, orthogonal init. Training: 16 parallel envs, 3,000,000 total steps, ~639s on a Kaggle T4 GPU. Evaluation: 20 deterministic (argmax) episodes, meanreward=219.32, stdreward=23.46. Load pposcratchmodel.pt into the Agent class defined in pposcratch.py (also included in this repo) and run deterministic argmax actions on the actor logits.
Independent publisher
Aiman
muaibinrah
Models in Library3
Datasets in Library0
Models on Hugging Face14
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Published tasks: Reinforcement learning.
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Models
This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library (HF Deep RL Course Unit5). Final training (run-id Pyramids1): Step 990000, Mean Reward 1.662, Std of Reward 0.400 (checkpoint exported at Step 1000160). Download the.onnx file and use with the ML-Agents Pyramids demo, or watch it play at https://huggingface.co/spaces/unity/ML-Agents-Pyramids
This is a trained model of a ppo agent playing SnowballTarget using the Unity ML-Agents Library (HF Deep RL Course Unit5). Download the.onnx file and use with the ML-Agents SnowballTarget demo, or watch it play at https://huggingface.co/spaces/ThomasSimonini/ML-Agents-SnowballTarget