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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".