Lucas Lehnert

We don’t have enough information about this author to calculate their statistics. If you think this is an error let us know.
Learn More
Curiosity towards exploring new objects in one's environment is a key driver of intelligent agents. We explore the problem of mapping in environments which are non-stationary, and where areas may exhibit different change patterns. This is an important challenge for potential " domestic " robots, which would have to perform tasks in houses. We propose a(More)
One question central to Reinforcement Learning is how to learn a feature representation that supports algorithm scaling and re-use of learned information from different tasks. Successor Features approach this problem by learning a feature representation that satisfies a temporal constraint. We present an implementation of an approach that decouples the(More)
Off-policy learning refers to the problem of learning the value function of a behaviour, or policy, while selecting actions with a different policy. Gradient-based off-policy learning algorithms, such as GTD (Sutton et al., 2009b) and TDC/GQ (Sutton et al., 2009a), converge when selecting actions with a fixed policy even when using function approximation(More)
  • 1