FPGA implementation of multiple Pursuit-Evasion games with decentralized Learning Automata
Bibliographic record
Abstract
This paper addresses the implementation of multiple Pursuit-Evasion (PE) games using Field Programmable Gate Array (FPGA) technology. The multi-agent game is modeled as Markov chains with each player working as a decentralized unit and using Learning Automata (LA). To take a desired action at each step for each player, an efficient Learning algorithm is used that leads to the players to evolve and adapt to the environment in order to solve difficult problems. To realize the PE game in the hardware devices, such as FPGAs in this paper, the system is optimized and designed based on the properties of the hardware technology. The implementation approaches for the realization of the main building blocks of the system are presented in detail. A modified Learning algorithm is used in the hardware implementation. This system has been developed in VHSIC Hardware Description Language (VHDL) and implemented using Xilinx Virtex 6 FPGAs. The simulation results have been achieved and presented in this paper. To prove the efficiency of the Learning algorithm designed with hardware technology, the simulation results are also presented in statistic version, which further proves that the speed of capture is decreased after using the Learning algorithm and finally converges to an equilibrium point in this multiple PE games.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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".