Specialised Technical Course
Reinforcement Learning for Optimization and Control
Develop a structured reinforcement learning problem by defining an environment, comparing reward choices and evaluating learned behaviour against control constraints.
Course Overview
Examines reinforcement learning for optimisation and control problems. Participants review environment design, reward choices and the constraints involved in evaluating learned behaviour.
Who This Course Is For
- Machine learning specialists exploring control
- Engineers evaluating industrial optimisation approaches
- Software developers designing learning experiments
- Technical researchers reviewing policy behaviour
What Participants Will Learn
Frame optimisation objectives and control constraints
Define environment states actions and assumptions
Assess reward choices and unintended behaviour
Compare policies using explicit evaluation criteria
Identify risks before operational control use
Key topics
- Environment design
- Reward definition
- Policy evaluation
- Control constraints
Course Modules
Back to Course OverviewReinforcement Learning and Control Context
Reinforcement Learning and Control Context
Module overview
Review environment design, reward definition, policy evaluation and constraints as parts of a reinforcement learning control problem.
Key topics
Environment design · Reward definition · Policy evaluation · Control constraints
Learning outcome
Identify the main assumptions and constraints in a proposed learning problem.
Learning format
Technical explanation · worked examples · case review
Environment and Problem Formulation
Environment and Problem Formulation
Module overview
Define the environment, observations and available actions needed to represent a control or optimisation problem.
Key topics
Environment assumptions · Observation design · Action choices · Control objectives
Learning outcome
Outline a problem formulation and identify limits in its representation.
Learning format
Technical explanation · worked examples · case review
Rewards and Behaviour Trade-offs
Rewards and Behaviour Trade-offs
Module overview
Compare reward definitions and consider how they may encourage behaviour that conflicts with control objectives.
Key topics
Reward definition · Objective trade-offs · Unintended behaviour · Constraint representation
Learning outcome
Explain a reward choice and identify behaviour requiring further evaluation.
Learning format
Technical explanation · worked examples · case review
Policy Evaluation and Constraints
Policy Evaluation and Constraints
Module overview
Assess learned behaviour against stated objectives, control constraints and assumptions about the evaluation environment.
Key topics
Policy evaluation · Control constraints · Evaluation scenarios · Evidence limitations
Learning outcome
Define evaluation criteria and identify risks not resolved by a single experiment.
Learning format
Technical explanation · worked examples · case review
Control Experiment Review
Control Experiment Review
Module overview
Review a learning experiment and present its environment assumptions, reward reasoning and evaluation risks.
Key topics
Software experiment · Control case study · Policy comparison · Risk review
Learning outcome
Participants can frame a reinforcement learning problem and identify evaluation risks.
Learning format
Lectures, software experiments and control case studies
Programme Information
Course duration, location, practical components, training-centre information and applicable certification arrangements depend on the selected programme.
Learning format: Lectures, software experiments and control case studies
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