Reinforcement Learning POCs

Your Reinforcement Learning POC Company

Reinforcement learning as a technique has massive potential. But there are risks.

Some problems are not a good fit, so you want to be sure that you’re not wasting your money. Our reinforcement learning proof-of-concepts are a way of exploring the value potential without committing to long term funding.

Although all of our POCs differ, they generally deliver a rough prototype to prove that the riskiest parts of the project will be successful, or not. Our reinforcement learning POCs generally take the form of a rough, but working agent, and validation that it can achieve what we set out to solve. The goal is a demonstration that the idea is technically feasible, given the data and current technology capabilities.

Our reinforcement learning POCs are then promoted into a reinforcement learning development project, where we design, build, and deliver practical artificial intelligence solutions.

World Leading RL Company

Winder.AI predominantly works on projects that involve developing RL solutions for domain specific problems.

Take CMPC, who are one of the world’s largest paper manufacturers, as an example. They have a complicated paper bleaching process, which had some level of ML intervention to help optimise the process. However the ML model could not take the multi-step process into account; i.e. it could not learn that changing some parameter early in the process could catastrophically alter the end result.

We ran a POC that proved that RL was capable of learning these complex multi-step interactions and they are working on incorporating it into their production process.

We can help you too, no matter what industry you are in. We operate under all contract types, from fixed cost proof-of-concepts to ongoing time and materials expertise.

Whatever your business, we can help.

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Reinforcement Learning POC Process

1. Business Context

Any problem demands context from the business. A solution for one industry may not be applicable to another, nor is every business the same. Establishing shared context helps get the project off to the right start.

2. Domain Knowledge Transfer

Businesses are often experts in their own domain. This domain expertise is valuable to help direct future solutions.

3. Problem Definition/Clarification

POCs usually start with a vague idea of what problem they are trying to solve. But the problem definition often changes over time, becoming more concrete, adapting to what is possible given the data.

4. MDP Design

The formulation and definition of the Markov Decision Process is a crucial part of the solution design and is often refined over a number of iterations.

5. Environment Definition/Creation

Defining the environment can take some time to get right, because it must be representative, and it’s unusual to allow the use of a real system.

6. RL Agent Development

The development of first a baseline agent, then a sophisticated RL agent is take in stages to ensure the MDP and environment actually represent the problem. It’s important to keep validating the solution.

7. Agent Evaluation and Analysis

Agents can take a while to train, especially if they are in complex environments. In this phase we validate that the problem is viable and hopefully produce promising results.

8. Reporting

Once models are validated then it’s time to report the results back to the stakeholders. After this phase we often start looking at another problem, or promote it to a fully-fledged reinforcement learning development project.

Optimizing for Value Generation

Businesses have three core operational functions. Processes define how businesses run. Decisions decide when businesses are run. Strategies define why businesses are run.

Software has successfully automated many business processes. Data science automates decisions and strategies via machine learning and reinforcement learning, respectively.

By leveraging our reinforcement learning services we can help you automate the top two most valuable tiers in the pyramid, to make your organization more efficient and profitable.