2,351 research outputs found
Factorized Q-Learning for Large-Scale Multi-Agent Systems
Deep Q-learning has achieved significant success in single-agent decision
making tasks. However, it is challenging to extend Q-learning to large-scale
multi-agent scenarios, due to the explosion of action space resulting from the
complex dynamics between the environment and the agents. In this paper, we
propose to make the computation of multi-agent Q-learning tractable by treating
the Q-function (w.r.t. state and joint-action) as a high-order high-dimensional
tensor and then approximate it with factorized pairwise interactions.
Furthermore, we utilize a composite deep neural network architecture for
computing the factorized Q-function, share the model parameters among all the
agents within the same group, and estimate the agents' optimal joint actions
through a coordinate descent type algorithm. All these simplifications greatly
reduce the model complexity and accelerate the learning process. Extensive
experiments on two different multi-agent problems demonstrate the performance
gain of our proposed approach in comparison with strong baselines, particularly
when there are a large number of agents.Comment: 7 pages, 5 figures, DAI 201
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Towards Informed Exploration for Deep Reinforcement Learning
In this thesis, we discuss various techniques for improving exploration for deep reinforcement learning. We begin with a brief review of reinforcement learning (RL) and the fundamental v.s. exploitation trade-off. Then we review how deep RL has improved upon classical and summarize six categories of the latest exploration methods for deep RL, in the order increasing usage of prior information. We then explore representative works in three categories discuss their strengths and weaknesses. The first category, represented by Soft Q-learning, uses regularization to encourage exploration. The second category, represented by count-based via hashing, maps states to hash codes for counting and assigns higher exploration to less-encountered states. The third category utilizes hierarchy and is represented by modular architecture for RL agents to play StarCraft II. Finally, we conclude that exploration by prior knowledge is a promising research direction and suggest topics of potentially impact
Reinforcement in Cooperative Games
Εθνικό Μετσόβιο Πολυτεχνείο--Μεταπτυχιακή Εργασία. Διεπιστημονικό-Διατμηματικό Πρόγραμμα Μεταπτυχιακών Σπουδών (Δ.Π.Μ.Σ.) “Επιστήμη Δεδομένων και Μηχανική Μάθηση
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