7th International Conference on Machine Learning and Computing (ICMLC 2015), Floransa, İtalya, 19 Mart 2015, cilt.5, sa.5, ss.353-358
A reinforcement learning (RL) agent mostly
assumes environments are stationary which is not feasible on
most real world problems. Most RL approaches adapt slow
changes by forgetting the previous dynamics of the
environment. Reinforcement learning-context detection
(RL-CD) is a technique that helps determine changes of the
environment’s nature which the agent with the capability to
learn different dynamics of the non-stationary environment. In
this study we propose an autonomous agent that learns a
dynamic environment by taking advantage of hierarchical
reinforcement learning (HRL) and present how the hierarchical
structure can be integrated into RL-CD to speed up the
convergence of a policy.