Reinforcement learning algorithm for non-stationary environments

Learning in Non-stationary Environments

P-MARL: Prediction-Based Multi-Agent Reinforcement Learning for Non-Stationary Environments

Multi-Agent Reinforcement Learning (MARL) is a widely-used technique for optimization in decentralised control problems, addressing complex challenges when several agents change actions simultaneously and without collaboration. Such challenges are exacerbated when the environment in which the agents learn is inherently non-stationary, as agents’ actions are then non-deterministic. In this paper...

Reinforcement Learning in Non-Markov Environments

Recently, techniques based on reinforcement learning (RL) have been used to build systems that learn to perform non-trivial sequential decision tasks. To date, most of this work has focussed on learning tasks that can be described as Markov decision processes (MDPs). While MDPs are useful for modeling a wide range of control problems, there are important problems that are inherently non-Markov....

A noise-estimation algorithm for highly non-stationary environments

A noise-estimation algorithm is proposed for highly non-stationary noise environments. The noise estimate is updated by averaging the noisy speech power spectrum using time and frequency dependent smoothing factors, which are adjusted based on signal-presence probability in individual frequency bins. Signal presence is determined by computing the ratio of the noisy speech power spectrum to its ...

Multi-model Approach to Non-stationary Reinforcement Learning

This paper proposes a novel alogrithm for a class of nonstationary reinforcement learning problems in which the environmental changes are rare and finite. Through discarding corrupted models and combining similar ones, the proposed algorithm maintains a collection of frequently encountered environment models and enables an effective adaptation when a similar environment recurs. The algorithm ha...