Closed shizhec closed 2 months ago
how can I acquire the dynamic model of those Mujoco tasks?
Not clear what you are asking here. Computing the dynamics is exactly what the library does. MuJoCo is the dynamics.
Regarding this
I also found in the Gymnasium repo, there is an issue require the same thing but no solutions yet.
@Kallinteris-Andreas is basically giving the same answer I did.
Regarding this
I also found in the Gymnasium repo, there is an issue require the same thing but no solutions yet.
@Kallinteris-Andreas is basically giving the same answer I did.
Hi yuval @yuvaltassa , Thanks for your time and the answer, and sorry that I am still a bit confuse about it. Regarding the dynamic step function I provide for the pendulum example. I found it in some MPC works like this https://github.com/locuslab/mpc.pytorch. It takes the current state and action, and output the next state, so that MPC can predict a sequence of state using an action sequence and compute the cost to optimise the action. I'm wondering if I could have the same step function that takes as input the current state and action of the Mujoco robot and return the next state (qpos, qvel)? Or if you suggest that I can just create another env and set the state and do the gym.step?
Hi,
I'm a student and I am new to the optimal control field. I'm trying to use MPC to solve the gymnasium Mujoco environments for tasks like Walker2d, Ant, Hopper etc. Since MPC requires a prior knowledge on the dynamic model of the problem, I am wondering how can I acquire the dynamic model of those Mujoco tasks?
I am using the Python Mujoco package version 2.3.7 with a gymnasium version 0.29.1. So the dynamic in python version would be preferred.
for the reference of the dynamic, here is the python version of the dynamic step functions of the pendulum task in the gym:
I also found in the Gymnasium repo, there is an issue require the same thing but no solutions yet. Farama-Foundation/Gymnasium#605
I'd appreciate it if someone could help me with this