Deep Reinforcement Learning Applied to IEEE Very Small Size Soccer Strategy
Why this work is in the frame
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Bibliographic record
Abstract
This work concerns a virtual learning framework to train and develop specific behaviours for robots in the context of the IEEE Very Small Size Soccer competition based on Deep Reinforcement Learning. The agents are required to learn how to intercept the ball on the player's side of the field in the presence of an adversary robot nearby. The Proximal Policy Optimization algorithm, augmented by Curriculum Learning, is used during the training process. The performance of the trained agents was evaluated and compared to those, in the same scenario, but without the Curriculum Learning technique and also to agents following previously used heuristic behaviors. As a result, agents trained with Curriculum Learning outperformed, with an success rate of 79.90%, both agents trained without Curriculum Learning and agents that used the chosen heuristic behaviour, which attained a success rate of 72.10% and 45.50%, respectively.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it