As applications for artificially intelligent agents increase in complexity we can no longer rely on clever heuristics and hand-tuned behaviors to develop their programming. Even t...
Shawn Arseneau, Wei Sun, Changpeng Zhao, Jeremy R....
Reinforcement learning (RL) can be impractical for many high dimensional problems because of the computational cost of doing stochastic search in large state spaces. We propose a ...
The Trading Agent Competition in its category Supply Chain Management (TAC SCM) is an international forum where teams construct agents that control a computer assembly company in ...
PAC-MDP algorithms approach the exploration-exploitation problem of reinforcement learning agents in an effective way which guarantees that with high probability, the algorithm pe...
: This paper analyses the requirements of automation and adaptation in the so called perceptive environments. These environments are places with the ability of perceiving the conte...