metadata
library_name: ml-agents
tags:
- reinforcement-learning
- deep-reinforcement-learning
- SnowballTarget
- ML-Agents-SnowballTarget
model-index:
- name: ppo-SnowballTarget
results:
- task:
type: reinforcement-learning
name: Reinforcement Learning
dataset:
name: ML-Agents-SnowballTarget
type: ML-Agents-SnowballTarget
metrics:
- name: mean_reward
type: mean_reward
value: 24.75 +/- 2.384848003542364
PPO agent playing SnowballTarget
Trained from scratch for 91,608 steps in Google Colab using Unity ML-Agents, with coding and execution assistance from Codex. This is an introductory course model, not a fully converged policy.
Evaluation
Evaluated using the exported ONNX policy with deterministic actions over 20 completed agent episodes, seed 12345. Mean reward: 24.75; standard deviation: 2.384848003542364. Soccer rewards include the team reward. Full episode returns are in evaluation.json.
Files
The ONNX file is the evaluated policy. configuration.yaml and config.json contain training settings. checkpoint.pt permits continued training; training.log records the actual run.