What is an RL environment? A practical guide for training AI agents
A plain definition, the five parts of a training environment for AI agents, and what it takes to build one that teaches the right lessons.
Articles
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RL environments vs. human-labelled data: which does your agent need?
Demonstrations, preference rankings and environments with verifiable rewards teach different things. The right choice follows from what you want the model to learn.
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What makes a good RL environment? 10 properties to check
A quality bar for RL environments, written as ten properties with a short test for each. Use it to judge an environment you build, borrow or buy.
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How to build an RL environment for an AI agent, step by step
A ten-step build sequence for RL environments, and a short Python sketch of a toy environment with reset, step, submit and score.
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Gym-style APIs for LLM agents: reset, step, submit and score
The classic reset-and-step interface still works for language agents, with a few changes. Here is how it maps, with an HTTP version you can copy.
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Reinforcement learning with verifiable rewards (RLVR), explained
RLVR rewards a model only when code can verify its output. Here is where the idea comes from, where it stops, and how it extends from maths and code to real workflows.
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Deterministic verifiers vs. LLM-as-a-judge: how to score agent work
Code or a model as the judge? When each one is right for scoring agent work, what goes wrong with LLM judges, and how to combine the two safely.
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Reward hacking in AI agents: what it is and how to prevent it
Agents optimise the score, not the intention behind it. What reward hacking looks like, from game agents to frontier models editing tests, and the defences that work.
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Solver-computed reference solutions: constraint solvers as ground truth
A frozen, solver-computed reference gives every score a fixed target. How to model a task, compute and freeze the reference, score against it, and where the method stops.
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Scoring agent tasks: gates, weights and partial credit
One pass or fail bit says too little, and naive partial credit can be gamed. A layered design, with a worked example, the agent that cheats and the tests that keep the weights honest.
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Honesty checks: catching agents that claim work they didn't do
An agent's final report is a claim, not a fact. How to score it against the final state, so that honest partial success beats confident false success.
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Which workflows make good RL environments?
Not every workflow makes a good training world. A six-part scorecard for choosing, applied to five back-office domains, with a decision rule and what to do with work that is hard to verify.
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Labour-law rules as code: modelling working-time limits for AI agents
Daily rest, weekly rest and working-time limits, checked on the official texts of EU, UK and German law, and turned into code that a verifier can run on any roster.
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Realistic mess: why training worlds need noisy documents and conflicting rules
Real work arrives as scans, typos, outdated documents and rules that contradict each other. How to put that mess into a training world, generated from seeds, while the verifier stays exact.
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How to evaluate AI agents: benchmarks, environments and their limits
Success rates are only the start. What agent evaluation should measure, what six public benchmarks test and how they score, where public benchmarks stop, and how private environments fill the gap.
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Reproducible agent evaluation: seeds, frozen references and episode logs
Agent scores vary for reasons that have nothing to do with the agent. Where the variation comes from, which parts can be fixed, and what to record so that any score can be repeated and checked later.
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Benchmark contamination: why private, licensed environments matter
When evaluation tasks leak into training data, scores stop measuring skill. What contamination is, how it is detected, and why tasks generated from seeds, kept private and licensed, are a practical defence.
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Egocentric video for robot learning: a practical guide
First-person video shows hands doing real work from the worker's point of view. What it teaches robots, how researchers use it, where public datasets stop, and what to ask for when you need more.
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Teleoperation, simulation or human video? Three sources of robot training data
Robots learn from three main kinds of data. What teleoperation, simulation and human video each provide, where each falls short, and how teams combine them.
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What makes robot training video useful? A capture and labelling checklist
Useful robot training video is decided at capture time. A checklist from the recording side: where the camera goes, what must stay in view, how to label steps, and what a delivery contains.
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Consent and privacy in real-world video datasets: a GDPR primer
Recording real work for robot learning means recording people. A plain-language primer on the GDPR rules that matter for video datasets, and a workflow that puts privacy first.