Independent Learning Approaches: Overcoming Multi-Agent Learning Pathologies In Team-Games

Deep Neural Networks enable Reinforcement Learning (RL) agents to learn behaviour policies directly from high-dimensional observations. As a result, the field of Deep Reinforcement Learning (DRL) has seen a great number of successes. Recently the sub-field of Multi-Agent DRL (MADRL) has received an increased amount of attention. However, considerations are required when using RL in Multi-Agent Systems. For instance Independent Learners (ILs) lack the convergence guarantees of many single-agent RL approaches, even in domains that do not require a MADRL approach. Furthermore, ILs must often overcome a number of learning pathologies to converge upon an optimal joint-policy. Numerous IL approaches have been proposed to facilitate cooperation, including hysteretic Q-learning (Matignon et al., 2007) and leniency (Panait et al., 2006). Recently LMRL2, a variation of leniency, proved robust towards a number of pathologies in low-dimensional domains, including miscoordination, relative overgeneralization, stochasticity, the alter-exploration problem and the moving target problem (Wei and Luke, 2016). In contrast, the majority of work on ILs in MADRL focuses on an amplified moving target problem, caused by neural networks being trained with potentially obsolete samples drawn from experience replay memories. In this thesis we combine advances from research on ILs with DRL algorithms. However, first we evaluate the robustness of tabular approaches along each of the above pathology dimensions. Upon identifying a number of weaknesses that prevent LMRL2 from consistently converging upon optimal joint-policies we propose a new version of leniency, Distributed-Lenient Q-learning (DLQ). We find DLQ delivers state of the art performances in strategic-form and Markov games from Multi-Agent Reinforcement Learning literature. We subsequently scale leniency to MADRL, introducing Lenient (Double) Deep Q-Network (LDDQN). We empirically evaluate LDDQN with extensions of the Cooperative Multi-Agent Object Transportation Problem (Bucsoniu et al., 2010), finding that LDDQN outperforms hysteretic deep Q-learners in domains with multiple dropzones yielding stochastic rewards. Finally, to evaluate deep ILs along each pathology dimension we introduce a new MADRL environment: the Apprentice Firemen Game (AFG). We find lenient and hysteretic approaches fail to consistently learn near optimal joint-policies in the AFG. To address these pathologies we introduce Negative Update Intervals-DDQN (NUI-DDQN), a MADRL algorithm which discards episodes yielding cumulative rewards outside the range of expanding intervals. NUI-DDQN consistently gravitates towards optimal joint-policies in deterministic and stochastic reward settings of the AFG, overcoming the outlined pathologies

Reinforcement Learning using Augmented Neural Networks

Neural networks allow Q-learning reinforcement learning agents such as deep Q-networks (DQN) to approximate complex mappings from state spaces to value functions. However, this also brings drawbacks when compared to other function approximators such as tile coding or their generalisations, radial basis functions (RBF) because they introduce instability due to the side effect of globalised updates present in neural networks. This instability does not even vanish in neural networks that do not have any hidden layers. In this paper, we show that simple modifications to the structure of the neural network can improve stability of DQN learning when a multi-layer perceptron is used for function approximation.Comment: 7 pages; two columns; 4 figure