Heuristic Reinforcement Learning
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- Purdue University
- Organisation type
- Academia
- Country
- United States of America
- Published
- 1 October 1965
- Authors
- M. Waltz, K. Fu
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Robotics
- Task
- System control
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Training data
- tokens
Training compute
The arithmetic performed to train the model, measured in floating-point operations. It is a measure of what the training run cost, not of how fast the finished model answers you.
- Training compute
- 1.1 × 10⁶ FLOP
- How it was established
- Hardware
Figure 10 shows their largest system is trained for 3h and was trained on an analog IBM 1620 that was simulated on a digital IBM 1710. Nordhaus, 2007 lists the IBM 1620 at 200 multiplications per second and doesn’t contain the 1710 Flops estimate: 0.5 * 3 * 60 * 60 * 200 = 1080000 = 1.08e6 Assumed utilization of 0.5
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Wall-clock time
- 3 hours
Figure 10
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Why it is tracked
- Historical significance,Highly cited
- Record confidence
- Speculative
Sources
Where this record came from and when it was last checked.
- Reference
- A heuristic approach to reinforcement learning control systems
- Last updated
- 28 November 2025
What the numbers mean
What this model is
Heuristic Reinforcement Learning was published by Purdue University, in the country recorded as United States of America, during October 1965. The category the publisher falls under is academia.
It works in the domain of Robotics, and is recorded as performing the task of system control.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Training and provenance
Training it took a computation budget of roughly 1.1 × 10⁶ FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.
It is tracked in the underlying dataset for one reason in particular: historical significance,Highly cited.
Answers
Heuristic Reinforcement Learning — common questions
Heuristic Reinforcement Learning— how many parameters does it have?
No parameter count has been published for it, which is why no memory or speed figure appears on this page.
Heuristic Reinforcement Learning— who created it?
It was published by Purdue University, based in United States of America, an organisation categorised as academia.
Heuristic Reinforcement Learning— when was it released?
It was published in October 1965. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
Heuristic Reinforcement Learning— what is it used for?
It works in the domain of Robotics, and is recorded as handling the task of system control. These are the areas it was designed around; they describe intent rather than a hard boundary.
Heuristic Reinforcement Learning— how much compute was used to train it?
Training consumed around 1.1 × 10⁶ FLOP. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.
Heuristic Reinforcement Learning— what GPU do I need to run it?
None. This is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
Heuristic Reinforcement Learning— is it open source?
The licensing was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.