# Model-based Reinforcement Learning

Basic methods:

**I2A**: Imagination-Augmented Agents- Many model-based RL methods are not robust because they treat the model as infallible and use it for classical planning. I2A uses a more implicit approach in which a rollout encoder RNN produces an embedding for each rollout, which are then stacked into a single
**imagination code**. The imagination code serves the input to a model-based policy model $\pi$. Furthermore, $\pi$ is not used directly, but used to define an additional cross-entropy loss for a model-free (A3C) policy $\hat{\pi}$ which is actually used for behaviour. - One model rollout is done for each action, with $\hat{\pi}$ followed thereafter. It is also found to be more efficient to pre-train the model.

- Many model-based RL methods are not robust because they treat the model as infallible and use it for classical planning. I2A uses a more implicit approach in which a rollout encoder RNN produces an embedding for each rollout, which are then stacked into a single
**MBMF**: Model-Based RL with Model-Free Fine-Tuning- World Models
- Learn a latent representation of image observations using a VAE, then train an RNN to output a probability distribution (Gaussian mixture) over the next latent vector. For a simple linear controller with only a few hundred parameters, an evolutionary algorithm can be used to optimise entirely within the βimaginaryβ world of model rollouts.

**MVE**: Model-based Value Expansion- Model and true reward function for short-term up to a planning horizon $H$, Q learning for long-term. Improve accuracy compared with using each alone
- DDPG on Half Cheetah. $H$ intuitive hyperparameter to tune.