State Representation for RL algorithms:

• Autoencoder based representation learning using auxiliary objectives to learn meaningful representations

• Objectives:

  • Reconstruction: base objective of the Autoencoder, predict a state given its latent representation
  • Forward: predict next state given current state and action
  • Inverse: predict action given current state and next state
  • Reward: predict reward given current state and next state

• Objectives can be coupled in every way but the encoder learns a single representation mixing every objective, to learn a unique representation for every objective one must create an AE instance for each objective

• Training is done using a dataset of expriences collected in a ReplayBuffer

• Once the model is trained we can discard the auxiliary models to only use the trained encoder to extract a state representation

• We can also use the trained models in a boostraping method to evaluate future states and actions