VASE: Variational Assorted Surprise Exploration for Reinforcement Learning

Understand

Exploration in environments with continuous control and sparse rewards remains a key challenge in reinforcement learning (RL).

• Recently, surprise has been used as an intrinsic reward that encourages systematic and efficient exploration.
• We introduce a new definition of surprise and its RL implementation named Variational Assorted Surprise Exploration (VASE).
• VASE uses a Bayesian neural network as a model of the environment dynamics and is trained using variational inference, alternately updating the accuracy of the agent's model and policy.