Learning to Fly -- a Gym Environment with PyBullet Physics for\n Reinforcement Learning of Multi-agent Quadcopter Control
Bibliographic record
Abstract
Robotic simulators are crucial for academic research and education as well as\nthe development of safety-critical applications. Reinforcement learning\nenvironments -- simple simulations coupled with a problem specification in the\nform of a reward function -- are also important to standardize the development\n(and benchmarking) of learning algorithms. Yet, full-scale simulators typically\nlack portability and parallelizability. Vice versa, many reinforcement learning\nenvironments trade-off realism for high sample throughputs in toy-like\nproblems. While public data sets have greatly benefited deep learning and\ncomputer vision, we still lack the software tools to simultaneously develop --\nand fairly compare -- control theory and reinforcement learning approaches. In\nthis paper, we propose an open-source OpenAI Gym-like environment for multiple\nquadcopters based on the Bullet physics engine. Its multi-agent and vision\nbased reinforcement learning interfaces, as well as the support of realistic\ncollisions and aerodynamic effects, make it, to the best of our knowledge, a\nfirst of its kind. We demonstrate its use through several examples, either for\ncontrol (trajectory tracking with PID control, multi-robot flight with\ndownwash, etc.) or reinforcement learning (single and multi-agent stabilization\ntasks), hoping to inspire future research that combines control theory and\nmachine learning.\n
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.006 |
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".