Unsupervised Control Through Non-Parametric Discriminative Rewards

Understand

Learning to control an environment without hand-crafted rewards or expert data remains challenging and is at the frontier of reinforcement learning research.

• We present an unsupervised learning algorithm to train agents to achieve perceptually-specified goals using only a stream of observations and actions.
• Our agent simultaneously learns a goal-conditioned policy and a goal achievement reward function that measures how similar a state is to the goal state.
• This dual optimization leads to a co-operative game, giving rise to a learned reward function that reflects similarity in controllable aspects of the environment instead of distance in the space of observations.