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Reinforcement learning algorithm for non-stationary environments


Reinforcement learning (RL) methods learn optimal decisions in the presence of a stationary environment. However, the stationary assumption on the environment is very restrictive. In many real world problems like traffic signal control, robotic applications, etc., one often encounters situations with non-stationary environments, and in these scenarios, RL methods yield sub-optimal decisions. In this paper, we thus consider the problem of developing RL methods that obtain optimal decisions in a non-stationary environment. The goal of this problem is to maximize the long-term discounted reward accrued when the underlying model of the environment changes over time. To achieve this, we first adapt a change point algorithm to detect change in the statistics of the environment and then develop an RL algorithm that maximizes the long-run reward accrued. We illustrate that our change point method detects change in the model of the environment effectively and thus facilitates the RL algorithm in maximizing the long-run reward. We further validate the effectiveness of the proposed solution on non-stationary random Markov decision processes, a sensor energy management problem, and a traffic signal control problem.

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Correspondence to Sindhu Padakandla.

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Padakandla, S., K. J., P. & Bhatnagar, S. Reinforcement learning algorithm for non-stationary environments. Appl Intell 50, 3590–3606 (2020).

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  • Markov decision processes
  • Reinforcement learning
  • Non-Stationary environments
  • Change detection