Environment
An environment is the external context, including its entities and conditions, in which a system, process, or agent exists or operates.
Platform Engineering on Kubernetes
An environment is a distinct deployment context in which an application and its supporting resources run.
- How it is used
- Provides distinct places to test releases, serve users, or give development teams isolated application instances.
- How deep it goes
- Works through configuration-based promotion and a provisionable development environment, while leaving broader platform configuration outside the walking skeleton.
- Examples
- Uses a Conference application development environment provisioned as a vcluster and an Environment Kubernetes resource showing its requested configuration.
- What you can do after
- Operate/configure it by requesting an isolated development environment through the platform API and connecting to the provisioned virtual cluster.
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ASP.NET Core in Action, 3e
A hosting environment is the runtime context that identifies where an application is running and determines environment-specific configuration and behavior.
- How it is used
- Uses hosting environments to select configuration values and application behavior for development, staging, and production.
- How deep it goes
- Walks through identifying an environment, loading its configuration, and setting it locally, without examining hosting internals.
- Examples
- Uses separate database settings, different logging levels, environment-specific appsettings.json files, and conditional exception handling.
- What you can do after
- Operate/configure it by setting an environment, checking it through IHostEnvironment, and selecting environment-specific settings or middleware.
Optimization Algorithms: AI techniques for design, planning, and control problems
An environment is the external set of conditions that interacts with an organism’s genotype to influence its characteristics.
- How it is used
- Distinguishes external influences on phenotype from genotype and identifies the world an agent interacts with in reinforcement learning.
- How deep it goes
- Defines genotype–environment interaction and elaborates reinforcement-learning environments through states, observations, actions, rewards, and transitions; does not model biological effects.
- Examples
- Uses a four-gene organism to illustrate genotype and phenotype and a commute to contrast certain and uncertain outcomes.
- What you can do after
- Evaluate/understand how phenotype relates to genotype and environment, and how an agent’s actions can change its environment.
Get Programming: Learn to code with Python
- How it is used
- Uses environments to explain how modules and function calls keep internal work separate while passing needed results outward.
- How deep it goes
- Traces function calls through parameter mapping, local variables, returned values, and the end of a scope.
- Examples
- Uses independent researchers in a group project and a word-length function call to illustrate separate working spaces.
- What you can do after
- Understand which variables belong to a function call and how inputs and return values cross its boundary.
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Grokking AI Algorithms, Second Edition
An environment is the external setting in which an agent acts and receives consequences or feedback, including a simulated setting that models a market.
- How it is used
- Provides the setting where a reinforcement-learning agent takes actions and receives feedback toward a goal.
- How deep it goes
- Works through a simplified simulation and its basic operations, while noting that real environments have more varied actions and consequences.
- Examples
- Uses dog training, parking-lot navigation, and simulated financial trading to show different settings for action and feedback.
- What you can do after
- Evaluate/understand how to model a setting for agent practice and why its reward rules can produce unwanted behavior.
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Terraform in Depth: Infrastructure as Code with Terraform and OpenTofu
An environment is an isolated instance of a software system with independently managed infrastructure, configuration, and state.
- How it is used
- Uses isolated infrastructure instances to support development, testing, production, regional deployment, and customer installations.
- How deep it goes
- Explains lifecycle, environment types, variable-based variation, and thin root modules, but defers sensitive-value handling details.
- Examples
- Uses a Factory Analytics developer instance, staging and production flow, regional deployments, and an application-module configuration.
- What you can do after
- Operate environments via Terraform by applying isolated instances, configuring differences, and destroying temporary feature environments.
Build a Robo-Advisor with Python (From Scratch)
An environment is the external system in which a reinforcement-learning agent takes actions and from which it receives observations and rewards.
- How it is used
- Frames stock and bond markets as the setting where an investor-agent learns decisions through trial, error, and reward feedback.
- How deep it goes
- Models states with discretized wealth and time, including a log-wealth grid that reflects widening wealth dispersion.
- Examples
- Uses stock and bond markets, investor allocation, and a wealth-time grid for goals-based investing.
- What you can do after
- Evaluate/understand how to represent a financial environment with wealth states, time steps, investment actions, and reward feedback.
Read how Build a Robo-Advisor with Python (From Scratch) explains environment →
Machine Learning System Design
A computational representation of an online environment that generates or replays interactions for offline algorithm evaluation.
- How it is used
- Evaluates alternative models offline by replaying historical interactions and estimating product metrics.
- How deep it goes
- Explains the replay procedure, derived metrics, worked outcomes, and its non-ideal relationship to online performance.
- Examples
- Uses banner-to-landing-page transitions, purchases, product counts, conversion rates, and profit-per-click scenarios.
- What you can do after
- Evaluate different models against estimated online product metrics before broader deployment.
Read how Machine Learning System Design explains environment →
Related concepts
- reinforcement learningused by · Reinforcement learning is a machine-learning paradigm in which an agent learns actions or a policy through interaction with an environment and evaluative feedback, such as rewards.
- AI agentused by · An AI agent is a computational system that uses artificial intelligence to interpret information, select actions, and pursue goals through interaction with an environment.
- productionincludes · A production environment is the live operational setting in which software or other systems serve real users, workloads, or business processes.
- staging environmentincludes · A staging environment is a pre-production computing environment used to validate software or operational changes under conditions that approximate production.
- reward signalproduces · A reward signal is an evaluative feedback value representing the desirability or payoff of an action, outcome, or state.
- test environmentcontrasts with · A test environment is a configured computing context, including relevant software, infrastructure, data, and settings, in which software is tested.
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