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Felipe Leno Da Silva

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10 papers

1 author row

AAAI Conference 2026 Conference Paper

Machine Learning Models Assisting the Development of Antibody Therapeutics and Vaccines – an Emerging Trend

Felipe Leno Da Silva
Mikel Landajuela
Edwin A. Saada
Piyush Karande
Sudeep Sarma
Igor D'Angelo
Simone Conti
Daniel Faissol

The development of novel effective medical treatments is one of the most important and expected beneficial effects of the AI revolution. This decade is witnessing the rise of AI models able to predict complex properties for protein-protein interactions that hold great promise in assisting in the development of antibody therapeutics and vaccines, including for diseases that long eluded us in the pursuit of an effective treatment. This paper introduces this area of research in a language accessible to an AI researcher, exploring the biological problems that can be solved by AI models, as well as the general context to make solutions feasible in practical scenarios. We survey the main current trends and works in this research area and point towards current still unsolved challenges and trade offs. We expect this paper will be extremely helpful for AI researchers trying to join the field, as well as for researchers already working in one of the subtopics that wish to have a better understanding of the general context around it.

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AAAI Conference 2020 Short Paper

Providing Uncertainty-Based Advice for Deep Reinforcement Learning Agents (Student Abstract)

Felipe Leno Da Silva
Pablo Hernandez-Leal
Bilal Kartal
Matthew E. Taylor

The sample-complexity of Reinforcement Learning (RL) techniques still represents a challenge for scaling up RL to unsolved domains. One way to alleviate this problem is to leverage samples from the policy of a demonstrator to learn faster. However, advice is normally limited, hence advice should ideally be directed to states where the agent is uncertain on the best action to be applied. In this work, we propose Requesting Conﬁdence-Moderated Policy advice (RCMP), an action-advising framework where the agent asks for advice when its uncertainty is high. We describe a technique to estimate the agent uncertainty with minor modiﬁcations in standard value-based RL methods. RCMP is shown to perform better than several baselines in the Atari Pong domain.

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AAAI Conference 2020 Conference Paper

Uncertainty-Aware Action Advising for Deep Reinforcement Learning Agents

Felipe Leno Da Silva
Pablo Hernandez-Leal
Bilal Kartal
Matthew E. Taylor

Although Reinforcement Learning (RL) has been one of the most successful approaches for learning in sequential decision making problems, the sample-complexity of RL techniques still represents a major challenge for practical applications. To combat this challenge, whenever a competent policy (e. g. , either a legacy system or a human demonstrator) is available, the agent could leverage samples from this policy (advice) to improve sample-efﬁciency. However, advice is normally limited, hence it should ideally be directed to states where the agent is uncertain on the best action to execute. In this work, we propose Requesting Conﬁdence-Moderated Policy advice (RCMP), an action-advising framework where the agent asks for advice when its epistemic uncertainty is high for a certain state. RCMP takes into account that the advice is limited and might be suboptimal. We also describe a technique to estimate the agent uncertainty by performing minor modiﬁcations in standard value-function-based RL methods. Our empirical evaluations show that RCMP performs better than Importance Advising, not receiving advice, and receiving it at random states in Gridworld and Atari Pong scenarios.

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JAIR Journal 2019 Journal Article

A Survey on Transfer Learning for Multiagent Reinforcement Learning Systems

Felipe Leno Da Silva
Anna Helena Reali Costa

Multiagent Reinforcement Learning (RL) solves complex tasks that require coordination with other agents through autonomous exploration of the environment. However, learning a complex task from scratch is impractical due to the huge sample complexity of RL algorithms. For this reason, reusing knowledge that can come from previous experience or other agents is indispensable to scale up multiagent RL algorithms. This survey provides a unifying view of the literature on knowledge reuse in multiagent RL. We define a taxonomy of solutions for the general knowledge reuse problem, providing a comprehensive discussion of recent progress on knowledge reuse in Multiagent Systems (MAS) and of techniques for knowledge reuse across agents (that may be actuating in a shared environment or not). We aim at encouraging the community to work towards reusing all the knowledge sources available in a MAS. For that, we provide an in-depth discussion of current lines of research and open questions.

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JAAMAS Journal 2019 Journal Article

Agents teaching agents: a survey on inter-agent transfer learning

Felipe Leno Da Silva
Garrett Warnell
Peter Stone

Abstract While recent work in reinforcement learning (RL) has led to agents capable of solving increasingly complex tasks, the issue of high sample complexity is still a major concern. This issue has motivated the development of additional techniques that augment RL methods in an attempt to increase task learning speed. In particular, inter-agent teaching—endowing agents with the ability to respond to instructions from others—has been responsible for many of these developments. RL agents that can leverage instruction from a more competent teacher have been shown to be able to learn tasks significantly faster than agents that cannot take advantage of such instruction. That said, the inter-agent teaching paradigm presents many new challenges due to, among other factors, differences between the agents involved in the teaching interaction. As a result, many inter-agent teaching methods work only in restricted settings and have proven difficult to generalize to new domains or scenarios. In this article, we propose two frameworks that provide a comprehensive view of the challenges associated with inter-agent teaching. We highlight state-of-the-art solutions, open problems, prospective applications, and argue that new research in this area should be developed in the context of the proposed frameworks.

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AAMAS Conference 2019 Conference Paper

Integrating Agent Advice and Previous Task Solutions in Multiagent Reinforcement Learning

Felipe Leno Da Silva

Reinforcement learning methods have successfully been applied to build autonomous agents that solve challenging sequential decisionmaking problems. However, agents need a long time to learn a task, especially when multiple autonomous agents are in the environment. This research aims to propose a Transfer Learning framework to accelerate learning by combining two knowledge sources: (i) previously learned tasks; and (ii) advice from a more experienced agent. The definition of such framework requires answering several challenging research questions, including: How to abstract and represent knowledge, in order to allow generalization and posterior reuse? , How and when to transfer and receive knowledge in an efficient manner? , and How to consistently combine knowledge from several sources?

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IJCAI Conference 2018 Conference Paper

Autonomously Reusing Knowledge in Multiagent Reinforcement Learning

Felipe Leno Da Silva
Matthew E. Taylor
Anna Helena Reali Costa

Autonomous agents are increasingly required to solve complex tasks; hard-coding behaviors has become infeasible. Hence, agents must learn how to solve tasks via interactions with the environment. In many cases, knowledge reuse will be a core technology to keep training times reasonable, and for that, agents must be able to autonomously and consistently reuse knowledge from multiple sources, including both their own previous internal knowledge and from other agents. In this paper, we provide a literature review of methods for knowledge reuse in Multiagent Reinforcement Learning. We define an important challenge problem for the AI community, survey the existent methods, and discuss how they can all contribute to this challenging problem. Moreover, we highlight gaps in the current literature, motivating "low-hanging fruit'' for those interested in the area. Our ambition is that this paper will encourage the community to work on this difficult and relevant research challenge.

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AAMAS Conference 2018 Conference Paper

Object-Oriented Curriculum Generation for Reinforcement Learning

Felipe Leno Da Silva
Anna Helena Reali Costa

Autonomously learning a complex task takes a very long time for Reinforcement Learning (RL) agents. One way to learn faster is by dividing a complex task into several simple subtasks and organizing them into a Curriculum that guides Transfer Learning (TL) methods to reuse knowledge in a convenient sequence. However, previous works do not take into account the TL method to build specialized Curricula, leaving the burden of a careful subtask selection to a human. We here contribute novel procedures for: (i) dividing the target task into simpler ones under minimal human supervision; (ii) automatically generating Curricula based on object-oriented task descriptions; and (iii) using generated Curricula for reusing knowledge across tasks. Our experiments show that our proposal achieves a better performance using both manually given and generated subtasks when compared to the state-of-the-art technique in two different domains.

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AAMAS Conference 2017 Conference Paper

Simultaneously Learning and Advising in Multiagent Reinforcement Learning

Felipe Leno Da Silva
Ruben Glatt
Anna Helena Reali Costa

Reinforcement Learning has long been employed to solve sequential decision-making problems with minimal input data. However, the classical approach requires a large number of interactions with an environment to learn a suitable policy. This problem is further intensified when multiple autonomous agents are simultaneously learning in the same environment. The teacher-student approach aims at alleviating 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. Even though previous works reported that an agent can learn faster when receiving advice, their proposals require that the teacher is an expert in the learning task. Sharing successful episodes can also accelerate learning, but this procedure requires a lot of communication between agents, which is unfeasible for domains in which communication is limited. Thus, we here propose a multiagent advising framework where multiple agents can advise each other while learning in a shared environment. If in any state an agent is unsure about what to do, it can ask for advice to other agents and may receive answers from agents that have more confidence in their actuation for that state. We perform experiments in a simulated Robot Soccer environment and show that the learning process is improved by incorporating this kind of advice.

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IJCAI Conference 2016 Conference Paper

Transfer Learning for Multiagent Reinforcement Learning Systems

Felipe Leno Da Silva
Anna Helena Reali Costa

Reinforcement learning methods have successfully been applied to build autonomous agents that solve many sequential decision making problems. However, agents need a long time to learn a suitable policy, specially when multiple autonomous agents are in the environment. This research aims to propose a Transfer Learning (TL) framework to accelerate learning by exploiting two knowledge sources: (i) previously learned tasks; and (ii) advising from a more experienced agent. The definition of such framework requires answering several challenging research questions, including: How to abstract and represent knowledge, in order to allow generalization and posterior reuse? , How and when to transfer and receive knowledge in an efficient manner? , and How to evaluate the transfer quality in a Multiagent scenario? .

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