no code implementations • 13 Apr 2022 • Ugo Lecerf, Christelle Yemdji-Tchassi, Pietro Michiardi
When learning to act in a stochastic, partially observable environment, an intelligent agent should be prepared to anticipate a change in its belief of the environment state, and be capable of adapting its actions on-the-fly to changing conditions.
no code implementations • 11 Apr 2022 • Ugo Lecerf, Christelle Yemdji-Tchassi, Sébastien Aubert, Pietro Michiardi
When learning to behave in a stochastic environment where safety is critical, such as driving a vehicle in traffic, it is natural for human drivers to plan fallback strategies as a backup to use if ever there is an unexpected change in the environment.