Abstract
The Courchevel environment is hereby published to ease the development of streaming machine learning algorithms. In first solving this problem, a rapid reinforcement learning algorithm was invented. A simple transformation is added to the Bellman equation, a principal pillar of AI, particularly for solving Markov Decision Problems. By adding stochasticity to Bellman, sustained Reward-Per-Episode gains of an order of magnitude are validated, for environments where the reward function is structurally anticipated to be multi-modal. Courchevel as a decision problem, a first solution, and the Biased Bellman innovation are revealed -- with accompanying data. For ease of discussion, Courchevel's dynamics are described in military terms.
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