Reinforcement Learning

From Wiki2
Revision as of 17:48, 10 July 2019 by Vnhxz (talk | contribs)
(diff) ← Older revision | Latest revision (diff) | Newer revision → (diff)
Jump to navigation Jump to search

Multi-Armed Bandit Examples

Image Ranking

Multi-Agent Learning

  • Stochastic games, Nash-Q, Gradient Ascent, WOLF, and Mean-field Q learning, particle swarm intelligence, Ant Colony Optimization (Colorni et al., 1991)
  • Game Theory in Smart Decentralised multi-agent RL
  • As above: It involves multi-agent reinforcement learning to compute the Nash equilibrium and Bayesian optimization to compute the optimal incentive, within a simulated environment. In the Prowler architecture, uses both MARL and Bayesian optimization in very clever ensemble to optimize the incentives in the network of agents. MARL is used to simulate the agents’ actions and produce the Nash equilibrium behavior by the agents for a given choice of parameter by the meta-agent. Bayesian optimization is used to select the parameters of the game that lead to more desirable outcomes. Bayesian optimizations find the best model based on randomness, which matches the dynamics of the system.


Git Repos


RTB image