New Zealand

Abdelkrim Nemra, and and Hacene Rezine (October 1st 2008). Genetic Reinforcement Learning Algorithms for On-line Fuzzy Inference System Tuning "Application to Mobile Robotic", Robotics, Automation and Control, Pavla Pecherkova, Miroslav Flidr and Jindrich Dunik, IntechOpen, DOI: 10.5772/5847. 

Dusko Katic and Miomir Vukobratovic (June 1st 2007). Reinforcement Learning Algorithms in Humanoid Robotics, Humanoid Robots: New Developments, Armando Carlos de Pina Filho, IntechOpen, DOI: 10.5772/4878. 

target network technique to stabilize residual algorithms, yielding a residual version of DDPG that significantly outperforms vanilla DDPG in the DeepMind Control Suite benchmark. Moreover, we find the residual algorithm an effective approach to the distribution mismatch problem in model-based planning. Compared with the existing TD(k) method, our residual-based method makes weaker assumptions about the model and yields a greater performance boost.

AAMAS 2020

Deep Residual Reinforcement Learning

residual algorithms

distribution mismatch

reinforcement learning

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technical paper

AAMAS is the leading scientific conference for research in autonomous agents and multi-agent systems. The AAMAS conference series was initiated in 2002 as the merging of three respected scientific meetings: the International Conference on Multi-Agent Systems (ICMAS), the International Workshop on Agent Theories, Architectures, and Languages (ATAL), and the International Conference on Autonomous Agents (AA). The aim of the joint conference is to provide a single, high-profile, internationally-respected archival forum for scientific research in the theory and practice of autonomous agents and multi-agent systems.

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AAMAS is the leading scientific conference for research in autonomous agents and multi-agent systems. The AAMAS conference series was initiated in 2002 as the merging of three respected scientific meetings: the International Conference on Multi-Agent Systems (ICMAS), the International Workshop on Agent Theories, Architectures, and Languages (ATAL), and the International Conference on Autonomous Agents (AA). 

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**Abstract:**

Crowdsourcing harnesses human effort to solve computer-hard problems. Such tasks often have different levels of difficulty and workers have varying levels of skill at completing them. With a limited budget, it is important to wisely allocate the spend among the tasks and workers such that the overall outcome is as good as possible. Most existing work addresses this budget allocation problem by assuming that workers have a single level of ability for all tasks. However, this neglects the fact that tasks can belong to a variety of diverse categories and workers may have varying abilities across them. To incorporating such category-awareness, we model the interaction between the crowdsource campaign initiator and the workers as a procurement auction and propose a computationally efficient mechanism, INCARE, to achieve high-quality outcomes given a limited budget. In this presentation, we will present INCARE in detail and show the performance of INCARE on a standard real-world data set.

A Budget-Limited Mechanism for Category-Aware Crowdsourcing Systems

Many recent works have discussed the propensity, or lack thereof, for emergent languages to exhibit properties of natural languages. A favorite in the literature is learning compositionality. We note that most of those works have focused on communicative bandwidth as being of primary importance. While important, it is not the only contributing factor. In this paper, we investigate the learning biases that affect the efficacy and compositionality of emergent languages. Our foremost contribution is to explore how capacity of a neural network impacts its ability to learn a compositional language. We additionally introduce a set of evaluation metrics with which we analyze the learned languages. Our hypothesis is that there should be a specific range of model capacity and channel bandwidth that induces compositional structure in the resulting language and consequently encourages systematic generalization. While we empirically see evidence for the bottom of this range, we curiously do not find evidence for the top part of the range and believe that this is an open question for the community.

Capacity, Bandwidth, and Compositionality in Emergent Language Learning

The presentation video of the paper "META-Learning State-based Eligibility Traces for More Sample-Efficient Policy Evaluation" for AAMAS 2020.

META-Learning State-based Eligibility Traces for More Sample-Efficient Policy Evaluation

Candidate Selections with Proportional Fairness Constraints

We consider an online resource allocation problem where tasks with specific values, sizes and resource requirements arrive dynamically over time, and have to be either serviced or rejected immediately. Reinforcement learning is a promising approach for this, but existing work on reinforcement learning has neglected that task owners may misreport their task requirements or values strategically when this is to their benefit. To address this, we apply mechanism design and propose a novel mechanism based on reinforcement learning that aims to maximise social welfare, is strategyproof and individually rational (i.e., truthful reporting and participation are incentivised). In experiments, we show that our algorithm achieves results that are typically within 90% of the optimal social welfare, while outperforming approaches that use fixed pricing (by up to 86% in specific cases).

Strategyproof Reinforcement Learning for Online Resource Allocation

In this work, we formalize and study the Moving Agents in Formation (MAiF) problem, that combines the tasks of finding short collision-free paths for multiple agents and keeping them in close adherence to a desired formation while they are moving. We develop a two-phase search algorithm, called SWARM-MAPF, whose first phase is inspired by swarm-based algorithms (in open regions) and whose second phase is inspired by multi-agent path-finding (MAPF) algorithms (in congested regions). In the first phase, SWARM-MAPF selects a leader among the agents and finds a path for it that is sufficiently far away from the obstacles so that the other agents can preserve the desired formation around it. It also identifies the critical segments of the leader's path where the other agents cannot preserve the desired formation and the refinement of which has thus to be delegated to the second phase. In the second phase, SWARM-MAPF refines these segments. Theoretically, we prove that SWARM-MAPF is complete. Empirically, we show that SWARM-MAPF scales well and is able to find close-to-optimal solutions.

Moving Agents in Formation in Congested Environments

Evolutionary optimization is a generic population-based metaheuristic that can be adapted to solve a wide variety of optimization problems and has proven very effective for combinatorial optimization problems. However, the potential of this metaheuristic has not been utilized in Distributed Constraint Optimization Problems (DCOPs), a well-known class of combinatorial optimization problems prevalent in Multi-Agent Systems. In this paper, we present a novel population-based algorithm, Anytime Evolutionary DCOP (AED), that uses evolutionary optimization to solve DCOPs. In AED, the agents cooperatively construct an initial set of random solutions and gradually improve them through a new mechanism that considers an optimistic approximation of local benefits. Moreover, we present a new anytime update mechanism for AED that identifies the best among a distributed set of candidate solutions and notifies all the agents when a new best is found. In our theoretical analysis, we prove that AED is anytime. Finally, we present empirical results indicating AED outperforms the state-of-the-art DCOP algorithms in terms of solution quality.

An Anytime Evolutionary DCOP Algorithm

Playing Games in the Dark: An approach for cross-modality transfer in reinforcements learning

Rationals vs Byzantine Players in Committee-Based Blockchains

Toward enabling next-generation robots capable of socially intelligent interaction with humans, we present a computational model of interactions in a social environment of multiple agents and multiple groups. The Multiagent Group Perception and Interaction (MGpi) network is a deep neural network that predicts the appropriate social action to execute in a group conversation (e.g., speak, listen, respond, leave), taking into account neighbors' observable features (e.g., location of people, gaze orientation, distraction, etc.). A central component of MGpi is the Kinesic-Proxemic-Message (KPM) gate, that performs social signal gating to extract important information from a group conversation. In particular, KPM gate filters incoming social cues from nearby agents by observing their body gestures (kinesics) and spatial behavior (proxemics). The MGpi network and its KPM gate are learned via imitation learning, using demonstrations from our designed social interaction simulator. Further, we demonstrate the efficacy of the KPM gate as a social attention mechanism, achieving state-of-the-art performance on the task of group identification without using explicit group annotations, layout assumptions, or manually chosen parameters.

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Next from AAMAS 2020

A Budget-Limited Mechanism for Category-Aware Crowdsourcing Systems

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Residual Flux Measurement of Power Transformer Based on Transient Current Difference

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