1,004 research outputs found
Reinforcement learning in large state action spaces
Reinforcement learning (RL) is a promising framework for training intelligent agents which learn to optimize long term utility by directly interacting with the environment. Creating RL methods which scale to large state-action spaces is a critical problem towards ensuring real world deployment of RL systems. However, several challenges limit the applicability of RL to large scale settings. These include difficulties with exploration, low sample efficiency, computational intractability, task constraints like decentralization and lack of guarantees about important properties like performance, generalization and robustness in potentially unseen scenarios.
This thesis is motivated towards bridging the aforementioned gap. We propose several principled algorithms and frameworks for studying and addressing the above challenges RL. The proposed methods cover a wide range of RL settings (single and multi-agent systems (MAS) with all the variations in the latter, prediction and control, model-based and model-free methods, value-based and policy-based methods). In this work we propose the first results on several different problems: e.g. tensorization of the Bellman equation which allows exponential sample efficiency gains (Chapter 4), provable suboptimality arising from structural constraints in MAS(Chapter 3), combinatorial generalization results in cooperative MAS(Chapter 5), generalization results on observation shifts(Chapter 7), learning deterministic policies in a probabilistic RL framework(Chapter 6). Our algorithms exhibit provably enhanced performance and sample efficiency along with better scalability. Additionally, we also shed light on generalization aspects of the agents under different frameworks. These properties have been been driven by the use of several advanced tools (e.g. statistical machine learning, state abstraction, variational inference, tensor theory).
In summary, the contributions in this thesis significantly advance progress towards making RL agents ready for large scale, real world applications
Interaction Pattern Disentangling for Multi-Agent Reinforcement Learning
Deep cooperative multi-agent reinforcement learning has demonstrated its
remarkable success over a wide spectrum of complex control tasks. However,
recent advances in multi-agent learning mainly focus on value decomposition
while leaving entity interactions still intertwined, which easily leads to
over-fitting on noisy interactions between entities. In this work, we introduce
a novel interactiOn Pattern disenTangling (OPT) method, to disentangle not only
the joint value function into agent-wise value functions for decentralized
execution, but also the entity interactions into interaction prototypes, each
of which represents an underlying interaction pattern within a subgroup of the
entities. OPT facilitates filtering the noisy interactions between irrelevant
entities and thus significantly improves generalizability as well as
interpretability. Specifically, OPT introduces a sparse disagreement mechanism
to encourage sparsity and diversity among discovered interaction prototypes.
Then the model selectively restructures these prototypes into a compact
interaction pattern by an aggregator with learnable weights. To alleviate the
training instability issue caused by partial observability, we propose to
maximize the mutual information between the aggregation weights and the history
behaviors of each agent. Experiments on both single-task and multi-task
benchmarks demonstrate that the proposed method yields results superior to the
state-of-the-art counterparts. Our code is available at
https://github.com/liushunyu/OPT
Deep Learning based Recommender System: A Survey and New Perspectives
With the ever-growing volume of online information, recommender systems have
been an effective strategy to overcome such information overload. The utility
of recommender systems cannot be overstated, given its widespread adoption in
many web applications, along with its potential impact to ameliorate many
problems related to over-choice. In recent years, deep learning has garnered
considerable interest in many research fields such as computer vision and
natural language processing, owing not only to stellar performance but also the
attractive property of learning feature representations from scratch. The
influence of deep learning is also pervasive, recently demonstrating its
effectiveness when applied to information retrieval and recommender systems
research. Evidently, the field of deep learning in recommender system is
flourishing. This article aims to provide a comprehensive review of recent
research efforts on deep learning based recommender systems. More concretely,
we provide and devise a taxonomy of deep learning based recommendation models,
along with providing a comprehensive summary of the state-of-the-art. Finally,
we expand on current trends and provide new perspectives pertaining to this new
exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys.
https://doi.acm.org/10.1145/328502
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