Hi Lin,
The nextObs variable returns the next state after transition from the current state by the Reinforcement Learning(RL) Agent. While training, using the train function, the step function is implicitly called, which takes the environment model for the RL agent and the action as input, and returns three outputs: nextObs,reward and isdone.These inputs are then used in the reward function to calculate the reward for the action taken.
The Train MBPO Agent to Balance Continuous Cart-Pole System example uses a rlNeuralNetworkEnvironment object to create the environment. In this function, you can provide a custom reward function by using the function handle. Refer to the following documentation link for this input paramater:
Once a custom reward function handle is provided, it is implicitly fed the input arguments (obs,action,nextObs) during training.
However, you can evaluate these function by using the step function (and get the nextObs variable) as shown in the following documentation section:
