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+=================================================
+Reinforcement Learning of Policies and Deployment
+=================================================
+
+We use several reinforcement learning frameworks to train policies for our robots.
+After the models have been trained we export them as onnx files which describe the neural network structure and weights.
+We use the bitbots_rl_motion policy execution framework to deploy the models on our robots.
+
+Policy Training
+===============
+
+Currently (August 2026) we use the following frameworks to train our policies:
+
+- Walking: `mujoco_playground <https://github.com/Flova/mujoco_playground/tree/kick>`_
+- Standup: mjlab (currently used) , `HoST <https://github.com/bit-bots/HoST>`_ (not in use currently), and `BeyondMimic <https://github.com/bit-bots/BeyondMimic>`_ (not in use currently)
+- Kick: model with proprietary training code from hightorque
+- Path following: `mjlab <https://github.com/bit-bots/mjlab_piplus>`_ (not in use currently)
+- Fun stuff like cartwheel: `BeyondMimic <https://github.com/bit-bots/BeyondMimic>`_
+
+Going forward we prefer using mjlab as, while it is slightly slower than mujoco_playground,
+it uses manager based reinforcement learning.
+Manager based reinforcement learning allows defining the RL problem as a set of configurations which are read by their specific managers.
+For example, the reward manager reads its configuration to call the appropriate reward functions and combine their output.
+
+We train the RL policies mostly on the GPU machines with the RTX 4090 and 5090 cards in our lab (cl05 and cl06).
+Training requires a lot of parameter tuning of the reward function and some other parameters of the environment like observation noise and domain randomization.
+
+
+Policy Deployment
+=================
+
+Policies are deployed on the robots using the `bitbots_rl_motion <https://github.com/bit-bots/bitbots_main/tree/main/src/bitbots_motion/bitbots_rl_motion>`_ package.
+To add a new policy, firstly add a node and configuration and add it to the launch file.
+To build the observation and process the action several handlers already exists but you may have to add new ones for your policy.
+The readme of the repository contains further information on the structure of the framework.
+
+An important aspect is the 
+
+.. code-block:: python
+
+    def allowed_states(self) -> bool:
+        """
+        Returns whether the policy is allowed to be executed in the current state.
+        This is used to prevent the robot from falling over when executing a policy.
+        """
+        raise NotImplementedError
+
+function. It defines in which states the policy is executed.
+
+To integrate the policy in the code, use ros topics, services, or actions which can be triggered from other components such as the HCM, behavior, or path planning.