From d941b2c9af6a877254526490e19392cda2ef417a Mon Sep 17 00:00:00 2001
From: Dominik Roth <dominik.roth.dev@gmail.com>
Date: Wed, 2 Oct 2024 18:45:11 +0200
Subject: [PATCH] Added RL to README

---
 README.md | 41 ++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 40 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 2612e3c..c4bc7f2 100644
--- a/README.md
+++ b/README.md
@@ -2,7 +2,7 @@
 
 NuCon is a Python library designed to interface with and control parameters in Nucleares, a nuclear reactor simulation game. It provides a robust, type-safe foundation for reading and writing game parameters, allowing users to easily create their own automations and control systems.
 
-In future versions, NuCon aims to implement built-in automation features.
+NuCon further provides a work in progress implementation of a reinforcement learning environment for training control policies.
 
 ## Features
 
@@ -83,6 +83,45 @@ Custom Enum Types:
 - `PumpOverloadStatus`: Enum for pump overload status (ACTIVE_AND_OVERLOAD, INACTIVE_OR_ACTIVE_NO_OVERLOAD)
 - `BreakerStatus`: Enum for breaker status (OPEN, CLOSED)
 
+## Reinforcement Learning (Work in Progress)
+
+NuCon includes a preliminary Reinforcement Learning (RL) environment based on the OpenAI Gym interface. This feature is currently a work in progress and requires additional dependencies.
+
+### Additional Dependencies
+
+To use the RL features, you'll need to install the following packages:
+
+```bash
+pip install gymnasium numpy
+```
+
+### RL Environment
+
+The `NuconEnv` class in `nucon/rl.py` provides a Gym-compatible environment for reinforcement learning tasks in the Nucleares simulation. Key features include:
+
+- Observation space: Includes all readable parameters from the Nucon system.
+- Action space: Encompasses all writable parameters in the Nucon system.
+- Step function: Applies actions to the Nucon system and returns new observations.
+- Objective function: Allows for custom objective functions to be defined for training.
+
+### Usage
+
+Here's a basic example of how to use the RL environment:
+```python
+from nucon.rl import NuconEnv
+
+env = NuconEnv(objectives=['max_power'], seconds_per_step=5)
+
+obs, info = env.reset()
+for _ in range(1000):
+    action = env.action_space.sample()  # Your agent here (instead of random)
+    obs, reward, terminated, truncated, info = env.step(action)
+
+    if terminated or truncated:
+        obs, info = env.reset()
+env.close()
+```
+
 ## Testing
 
 NuCon includes a test suite to verify its functionality and compatibility with the Nucleares game.