The Gambling environment is a single agent domain featuring discrete and continuous state and action spaces. Currently, one task is supported:
This environment corresponds to the version of the gambling problem described in Example 1.2 in Algorithms for Reinforcement Learning by Csaba Szepesvari (2010).
Future tasks will have more complex environments that an agent can plan such as:
cd gym-gambling pip install -e .