ppo-SnowballTarget-v1

Francesco-A/ppo-SnowballTarget-v1

ppo-SnowballTarget-v1 is a reinforcement learning model from Francesco-A. Use it for the reinforcement learning task on the model card, and read the license before you ship it in a product. It is set up for ml-agents. The card lists the license as apache-2.0.

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At a glance

Task: Reinforcement Learning
Library: ml-agents
License: apache-2.0
Access: Public
Created: Aug 13, 2023
Updated: Aug 14, 2023
SHA: 4e5f2ae5

Model card

From the Hugging Face model README

ppo Agent playing SnowballTarget

This is a trained model of a ppo agent playing SnowballTarget using the Unity ML-Agents Library.

Usage (with ML-Agents)

The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/

Watch the Agent play

You can watch the agent playing directly in your browser

Go to https://huggingface.co/spaces/ThomasSimonini/ML-Agents-SnowballTarget
Step 1: Find the model_id: Francesco-A/ppo-SnowballTarget-v1
Step 2: Select the .nn /.onnx file
Click on Watch the agent play

Training hyperparameters

behaviors:
  SnowballTarget:
    trainer_type: ppo
    summary_freq: 10000
    keep_checkpoints: 10
    checkpoint_interval: 55000
    max_steps: 250000
    time_horizon: 64
    threaded: true
    hyperparameters:
      learning_rate: 0.0003
      learning_rate_schedule: linear
      batch_size: 128
      buffer_size: 2048
      beta: 0.005
      epsilon: 0.2
      lambd: 0.95
      num_epoch: 3
    network_settings:
      normalize: false
      hidden_units: 256
      num_layers: 2
      vis_encode_type: simple
    reward_signals:
      extrinsic:
        gamma: 0.99
        strength: 1.0

Training details

Step	Time Elapsed	Mean Reward	Std of Reward	Status
10000	29.079 s	3.636	1.746	Training
20000	55.042 s	7.164	2.661	Training
30000	77.884 s	9.818	2.534	Training
40000	103.229 s	11.509	2.263	Training
50000	127.046 s	14.659	2.495	Training
60000	150.811 s	15.655	2.414	Training
70000	174.292 s	16.955	2.540	Training
80000	198.938 s	18.091	2.481	Training
90000	221.915 s	19.182	3.143	Training
100000	246.203 s	21.182	2.724	Training
110000	271.024 s	22.463	2.250	Training
120000	292.551 s	24.044	2.190	Training
130000	317.539 s	24.291	2.103	Training
140000	340.057 s	24.455	4.423	Training
150000	366.645 s	25.236	2.358	Training
160000	390.192 s	25.000	1.895	Training
170000	414.326 s	25.273	2.482	Training
180000	438.103 s	25.750	1.798	Training
190000	462.837 s	25.673	1.888	Training
200000	485.258 s	25.295	2.380	Training
210000	509.542 s	25.855	2.066	Training
220000	535.202 s	26.111	1.931	Training
230000	556.965 s	25.644	2.252	Training
240000	582.135 s	26.018	2.673	Training
250000	604.248 s	26.091	1.917	Training

Task: Reinforcement Learning
Library: ml-agents
License: apache-2.0
Created: Aug 13, 2023
Updated: Aug 14, 2023