Introduction

pycolab and AI Safety Gridworlds are older but still useful research tools for reinforcement learning experiments. pycolab gives you a grid-based game engine, while AI Safety Gridworlds builds on that style of environment to study reward hacking, specification problems, and other safety-related behaviors.

The easiest way to get started is to treat them as research code rather than polished end-user frameworks. Start with a clean virtual environment, verify that imports work, and spend time understanding the observation and reward loop before trying to train an agent.

What Each Project Does

pycolab is the lower-level building block. It lets you create ASCII-style worlds with sprites, drapes, actions, and rewards. You can think of it as a small engine for turn-based grid games used in RL research.

AI Safety Gridworlds sits closer to the research question. Instead of asking you to invent every environment yourself, it provides scenarios designed to expose behaviors such as:

• reward gaming
• unsafe shortcuts
• side effects
• mismatch between observed reward and intended performance

That makes the pairing useful: pycolab teaches you how the environment machinery works, and AI Safety Gridworlds gives you concrete tasks that illustrate why safety problems are subtle.

Start with a Clean Python Environment

These projects are not as turnkey as modern RL libraries, so isolating dependencies is worth the effort.

1python -m venv .venv
2source .venv/bin/activate
3python -m pip install --upgrade pip
4
5git clone https://github.com/google-deepmind/pycolab.git
6git clone https://github.com/google-deepmind/ai-safety-gridworlds.git
7
8python -m pip install -e ./pycolab
9python -m pip install -e ./ai-safety-gridworlds

After installation, verify the packages are importable:

1import pycolab
2import ai_safety_gridworlds
3
4print("pycolab:", pycolab.__file__)
5print("ai_safety_gridworlds:", ai_safety_gridworlds.__file__)

If those imports fail, fix the environment first. RL debugging is already difficult enough without mixing it with packaging problems.

Explore the Environment Modules Before Training

A useful first step is simply to discover what environments are available. This helps you understand the library layout before you write any learning code.

1import pkgutil
2import ai_safety_gridworlds.environments
3
4for module in pkgutil.iter_modules(ai_safety_gridworlds.environments.__path__):
5    print(module.name)

This is a practical way to browse the tasks shipped with the package. Once you know the available modules, you can open their source and inspect what observations, actions, rewards, and hidden performance measures they expose.

That inspection matters because safety environments often have two different ideas of success:

• the reward the agent sees
• the performance measure the researcher actually cares about

That gap is the whole point of many safety experiments.

Understand the Basic RL Loop

Even if you are not using a full training framework yet, it helps to think in terms of the standard loop:

1. reset or initialize the environment
2. observe the current state
3. choose an action
4. step the environment
5. receive reward and next observation
6. repeat until termination

With pycolab, you will usually spend more time looking at environment code than in a library-specific trainer. That is normal. The framework is designed for experimentation, and understanding the environment internals is part of using it well.

A good beginner path is:

• install both repositories from source
• run import checks
• inspect one small gridworld module
• print observations and rewards from a trivial random policy
• only then add a learning algorithm

Why AI Safety Gridworlds Is Worth Studying

Classic RL benchmarks often reward raw performance. Safety Gridworlds asks a harder question: can the agent get high visible reward while still behaving badly according to a more complete objective.

That makes the environments useful for:

• classroom demos about reward misspecification
• small-scale RL safety experiments
• debugging how an agent responds to incentives

Even if you later move to Gymnasium, PettingZoo, or custom simulators, the design lessons from these small environments carry over.

Common Pitfalls

• Expecting a modern plug-and-play API. These projects feel more like research code than production-ready tooling.
• Skipping source inspection. In safety work, understanding the hidden objective matters as much as the visible reward.
• Mixing package versions in the global interpreter instead of using a virtual environment.
• Trying to train a sophisticated agent before confirming that imports, observations, and actions behave as expected.
• Treating pycolab and AI Safety Gridworlds as interchangeable. One is a general environment engine; the other is a curated set of safety tasks.

Summary

• 'pycolab is a grid-based environment engine for RL experiments.'
• AI Safety Gridworlds provides safety-focused tasks built in that style.
• Install both in an isolated virtual environment and verify imports first.
• Explore shipped environment modules before writing training code.
• Focus on the observation, action, reward, and hidden-objective loop; that is where most beginner confusion comes from.