Author name cluster

Anna Costa

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

5 papers

1 author row

AAAI Conference 2017 Short Paper

Accelerating Multiagent Reinforcement Learning through Transfer Learning

Felipe da Silva
Anna Costa

Reinforcement Learning (RL) is a widely used solution for sequential decision-making problems and has been used in many complex domains. However, RL algorithms suffer from scalability issues, especially when multiple agents are acting in a shared environment. This research intends to accelerate learning in multiagent sequential decision-making tasks by reusing previous knowledge, both from past solutions and advising between agents. We intend to contribute a Transfer Learning framework focused on Multiagent RL, requiring as few domain-specific hand-coded parameters as possible.

AAAI Conference 2017 Short Paper

An Advising Framework for Multiagent Reinforcement Learning Systems

Felipe Silva
Ruben Glatt
Anna Costa

Reinforcement Learning has long been employed to solve sequential decision-making problems with minimal input data. However, the classical approach requires a long time to learn a suitable policy, especially in Multiagent Systems. The teacher-student framework proposes to mitigate this problem by integrating an advising procedure in the learning process, in which an experienced agent (human or not) can advise a student to guide her exploration. However, the teacher is assumed to be an expert in the learning task. We here propose an advising framework where multiple agents advise each other while learning in a shared environment, and the advisor is not expected to necessarily act optimally. Our experiments in a simulated Robot Soccer environment show that the learning process is improved by incorporating this kind of advice.

AAAI Conference 2017 Short Paper

Improving Deep Reinforcement Learning with Knowledge Transfer

Ruben Glatt
Anna Costa

Recent successes in applying Deep Learning techniques on Reinforcement Learning algorithms have led to a wave of breakthrough developments in agent theory and established the ﬁeld of Deep Reinforcement Learning (DRL). While DRL has shown great results for single task learning, the multi-task case is still underrepresented in the available literature. This D. Sc. research proposal aims at extending DRL to the multitask case by leveraging the power of Transfer Learning algorithms to improve the training time and results for multi-task learning. Our focus lies on deﬁning a novel framework for scalable DRL agents that detects similarities between tasks and balances various TL techniques, like parameter initialization, policy or skill transfer.

AAAI Conference 2017 Short Paper

Learning Options in Multiobjective Reinforcement Learning

Rodrigo Bonini
Felipe Silva
Anna Costa

Reinforcement Learning (RL) is a successful technique to train autonomous agents. However, the classical RL methods take a long time to learn how to solve tasks. Option-based solutions can be used to accelerate learning and transfer learned behaviors across tasks by encapsulating a partial policy into an action. However, the literature report only single-agent and single-objective option-based methods, but many RL tasks, especially real-world problems, are better described through multiple objectives. We here propose a method to learn options in Multiobjective Reinforcement Learning domains in order to accelerate learning and reuse knowledge across tasks. Our initial experiments in the Goldmine Domain show that our proposal learn useful options that accelerate learning in multiobjective domains. Our next steps are to use the learned options to transfer knowledge across tasks and evaluate this method with stochastic policies.

AAAI Conference 2017 Short Paper

Policy Reuse in Deep Reinforcement Learning

Ruben Glatt
Anna Costa

Driven by recent developments in Artiﬁcial Intelligence research, a promising new technology for building intelligent agents has evolved. The approach is termed Deep Reinforcement Learning and combines the classic ﬁeld of Reinforcement Learning (RL) with the representational power of modern Deep Learning approaches. It is very well suited for single task learning but needs a long time to learn any new task. To speed up this process, we propose to extend the concept to multi-task learning by adapting Policy Reuse, a Transfer Learning approach from classic RL, to use with Deep Q- Networks.