WorldModelWrapper#
- class torchrl.modules.WorldModelWrapper(*args, **kwargs)[source]#
World model wrapper.
This module wraps together a transition model and a reward model. The transition model is used to predict an imaginary world state. The reward model is used to predict the reward of the imagined transition.
- Parameters:
transition_model (TensorDictModule) – a transition model that generates a new world states.
reward_model (TensorDictModule) – a reward model, that reads the world state and returns a reward.
- get_reward_operator() TensorDictModule[source]#
Returns a reward operator that maps a world state to a reward.
- get_transition_model_operator() TensorDictModule[source]#
Returns a transition operator that maps either an observation to a world state or a world state to the next world state.