From e2e8db1f04e1d11858026dda17278d985c220f05 Mon Sep 17 00:00:00 2001 From: Dominik Roth Date: Thu, 12 Mar 2026 18:22:25 +0100 Subject: [PATCH] docs: remove WIP labels and clean up stale transitional prose Co-Authored-By: Claude Sonnet 4.6 --- README.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index fa21d74..50d1562 100644 --- a/README.md +++ b/README.md @@ -111,9 +111,9 @@ Custom Enum Types: So if you're not in the mood to play the game manually, this API can be used to easily create your own automations and control systems. Maybe a little PID controller for the rods? Or, if you wanna go crazy, why not try some -## Reinforcement Learning (Work in Progress) +## Reinforcement Learning -NuCon includes a preliminary Reinforcement Learning (RL) environment based on the OpenAI Gym interface. This allows you to train control policies for the Nucleares game instead of writing them yourself. This feature is currently a work in progress and requires additional dependencies. +NuCon includes a Reinforcement Learning (RL) environment based on the OpenAI Gym interface. This allows you to train control policies for the Nucleares game instead of writing them yourself. Requires additional dependencies. ### Additional Dependencies @@ -242,9 +242,9 @@ Predefined goal environments: - `Nucon-goal_power-v0`: target total generator output (3 × 0–1200 kW) - `Nucon-goal_temp-v0`: target core temperature (280–380 °C) -But theres a problem: RL algorithms require a huge amount of training steps to get passable policies, and Nucleares is a very slow simulation and can not be trivially parallelized. That's why NuCon also provides a +RL algorithms require a huge number of training steps, and Nucleares is slow and cannot be trivially parallelised. That's why NuCon provides a built-in simulator. -## Simulator (Work in Progress) +## Simulator NuCon provides a built-in simulator to address the challenge of slow training times in the actual Nucleares game. This simulator allows for rapid prototyping and testing of control policies without the need for the full game environment. Key features include: @@ -292,9 +292,9 @@ env = NuconEnv(simulator=simulator) # When given a similator, instead of waiting # ... ``` -But theres yet another problem: We do not know the exact simulation dynamics of the game and can therefore not implement an accurate simulator. Thats why NuCon also provides +The simulator needs an accurate dynamics model of the game. NuCon provides tools to learn one from real gameplay data. -## Model Learning (Work in Progress) +## Model Learning To address the challenge of unknown game dynamics, NuCon provides tools for collecting data, creating datasets, and training models to learn the reactor dynamics. Key features include: