Timing Actions in Games Through Bio-Inspired Reinforcement Learning
Bibliographic record
Abstract
A bio-inspired version of Reinforcement Learning (RL) can be used to learn to plan actions in a fully neuromorphic robot, allowing perception, processing, action planning, and control, maintaining an end-to-end spiking signal. Such an agent could fully take advantage of the sparse, low-power encoding and give insight into the secrets of biological intelligence. The current state-of-the-art in neuromorphic RL uses populations of neurons to implement traditional RL equations with novel spiking state-representation methods and achieves learning through weight updates of neural connections in an 8 × 8 grid world with discrete state definitions. We adapt and extend the algorithm towards a fully neuromorphic robot capable of playing highly dynamic games. In this paper we integrate the RL algorithm with a robot simulator for air hockey; doubling the dimensionality of the, now continuous, state. We demonstrate that we can adapt the method to learn precise ‘hit timing’, as the puck moves in front of the robot, the robot must choose the correct timing to intercept the puck, knocking it towards the opponent's goal. We also introduce a developmental approach to learning with Curriculum Learning (CL), allowing the robot to first learn a simple task, which can then be generalised and refined to more complex scenarios. The simplified air-hockey scenario demonstrates promising results for a fully neuromorphic pipeline in the future.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".