Hi Jahetbe,
Currently, there’s no implementation of Generative Flow Networks in MATLAB. Here’s a possible workaround for implementation of GFN:
- Define the environment:
a) States: Define the possible states in environment.
b) Actions: Define the actions that can be taken from each state.
c) Transition Model: A function that describes how actions transform states.
2. Define the reward function that assigns a scalar function to each terminal state.
3. Initialize the parameters of the neural network that will approximate the flow function.
4. Define the flow function (F(s,a)) that represents the probability of flow through the state-action pair.
5. Implement the learning algorithm which updates the parameters of the flow function based on the observed rewards.
a) Forward Sampling: Sample trajectories from the current policy.
b) Backward Flow Calculation: Compute the backward flow for each trajectory.
c) Parameter Update: Use a loss function to update the parameters of the flow network.
Hope this helps!!
