United Kingdom

Pay-per-last-$N$-shares&#39;&#39; (PPLNS) is one of the most common payout strategies used by mining pools in Proof-of-Work (PoW) cryptocurrencies such as Bitcoin.As with any payment scheme, it is imperative to study issues of incentive compatibility of miners within the pool. For PPLNS this question has only been partially answered; we know that reasonably-sized miners within a PPLNS pool prefer following the pool protocol over employing {\em specific} deviations. In this paper, we present a novel modification to PPLNS where we randomise the protocol in a natural way. We call our protocol &#39;&#39;Randomised pay-per-last-$N$-shares&#39;&#39; (RPPLNS), and note that the randomised structure of the protocol greatly simplifies the study of its incentive compatibility. We show that RPPLNS maintains the strengths of PPLNS (i.e., fairness, variance reduction, and resistance to pool hopping), while also being robust against a richer class of strategic mining than what has been shown for PPLNS.

AAMAS 2021

RPPLNS: Pay-per-last-N-shares with a Randomised Twist

AAMAS is the largest and most influential conference in the area of agents and multiagent systems, bringing together researchers and practitioners in all areas of agent technology and providing and internationally renowned high-profile forum for publishing and finding out about the latest developments in the field. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).  

![](https://assets.underline.io/uploads/markdown_image/1/image/ce4604bd54468a006164d16e080204fb.jpg)

Please click on the button below to register for this event.

If you are registered and see this page please check the account email you are logged in with. It must be the same as for the registration.

If you still have trouble accessing the contact please contact support at aamas2021@underline.io 

Pay-per-last-$N$-shares'' (PPLNS) is one of the most common payout strategies used by mining pools in Proof-of-Work (PoW) cryptocurrencies such as Bitcoin.As with any payment scheme, it is imperative to study issues of incentive compatibility of miners within the pool. For PPLNS this question has only been partially answered; we know that reasonably-sized miners within a PPLNS pool prefer following the pool protocol over employing {\em specific} deviations. In this paper, we present a novel modification to PPLNS where we randomise the protocol in a natural way. We call our protocol ''Randomised pay-per-last-$N$-shares'' (RPPLNS), and note that the randomised structure of the protocol greatly simplifies the study of its incentive compatibility. We show that RPPLNS maintains the strengths of PPLNS (i.e., fairness, variance reduction, and resistance to pool hopping), while also being robust against a richer class of strategic mining than what has been shown for PPLNS.

poster

Multiagent reinforcement learning (MARL) has achieved a remarkable amount of success in solving various types of video games. A cornerstone of this success is the auto-curriculum framework, which shapes the learning process by continually creating new challenging tasks for agents to adapt to, thereby facilitating the acquisition of new skills. In order to extend MARL methods to real-world domains outside of video games, we envision in this blue sky paper that maintaining a diversity-aware auto-curriculum is critical for successful MARL applications. Specifically, we argue that <i>behavioural diversity</i> is a pivotal, yet under-explored, component for real-world multiagent learning systems, and that significant work remains in understanding how to design a diversity-aware auto-curriculum. We list four open challenges for auto-curriculum techniques, which we believe deserve more attention from this community. Towards validating our vision, we recommend modelling realistic interactive behaviours in autonomous driving as an important test bed, and recommend the SMARTS/ULTRA benchmark.

Diverse Auto-Curriculum is Critical for Successful Real-World Multiagent Learning Systems (Blue Sky Ideas Track)

The real world is awash with multi-agent problems that require collective action by self-interested agents, from the routing of packets across a computer network to the management of irrigation systems. Such systems have local incentives for individuals, whose behavior has an impact on the global outcome for the group. Given appropriate mechanisms describing agent interaction, groups may achieve socially beneficial outcomes, even in the face of short-term selfish incentives. In many cases, collective action problems possess an underlying graph structure, whose topology crucially determines the relationship between local decisions and emergent global effects. Such scenarios have received great attention through the lens of network games. However, this abstraction typically collapses important dimensions, such as geometry and time, relevant to the design of mechanisms promoting cooperation. In parallel work, multi-agent deep reinforcement learning has shown great promise in modelling the emergence of self-organized cooperation in complex gridworld domains. Here we apply this paradigm in graph-structured collective action problems.

Modelling Cooperation in Network Games with Spatio-Temporal Complexity

Trading mechanisms play a fundamental role in the health of financial markets. For example, it is believed that continuous double auctions constitute fertile soil for predatory behaviour and toxic order flows. To this end, frequent call markets have been proposed as an alternative design choice to address the latency arbitrage opportunities built in those markets. This paper studies the extent to which adaptive rules to define the length of the clearing intervals affect the performance of frequent call markets.

Call Markets with Adaptive Clearing Intervals

From employment contracts to climate agreements, individuals often engage in groups that must reach decisions with varying levels of fairness. These dilemmas also pervade AI, e.g. in automated negotiation, conflict resolution or resource allocation. As evidenced by the Ultimatum Game, payoff maximization is frequently at odds with fairness. Eliciting equality in populations of self-regarding agents thus requires judicious interventions. Here we use knowledge about agents’ social networks to implement fairness mechanisms, in the context of Multiplayer Ultimatum Games. We show that preferentially attributing the role of Proposer to low-connected nodes enhances fairness. We further show that, when high-degree must be the Proposers, stricter voting rules (i.e., requiring consensus for collectives to accept a proposal) reduce unfairness.

Eliciting Fairness in Multiplayer Bargaining through Network-Based Role Assignment

Interactive reinforcement learning provides a way for agents to learn to solve tasks from evaluative feedback provided by a human user. Previous research showed that humans give copious feedback early in training but very sparsely thereafter. In this paper, we investigate the potential of agent learning from trainers' facial expressions via interpreting them as evaluative feedback. To do so, we implemented TAMER which is a popular interactive reinforcement learning method in a reinforcement-learning benchmark problem    Infinite Mario, and conducted the first large-scale study of TAMER involving 561 participants. With designed CNN-RNN model, our analysis shows that telling trainers to use facial expressions and competition can improve the accuracies for estimating positive and negative feedback using facial expressions. In addition, our results with a simulation experiment show that learning solely from predicted feedback based on facial expressions is possible and using strong/effective prediction models or a regression method, facial responses would significantly improve the performance of agents. Furthermore, our experiment supports previous studies demonstrating the importance of bi-directional feedback and competitive elements in the training interface.

Facial Feedback for Reinforcement Learning: A Case Study and Offline Analysis Using the TAMER Framework (JAAMAS Track)

In robotics, it is often not possible to learn useful policies using pure model-free reinforcement learning without significant reward shaping or  curriculum learning. As a consequence, many researchers rely on expert demonstrations to guide learning. However, acquiring expert  demonstrations can be expensive. This paper proposes an alternative approach where the solutions of previously solved tasks are used to produce an action prior that can facilitate exploration in future tasks. The action prior is a probability distribution over actions that summarizes the set of policies found solving previous tasks. Our results indicate that this approach can be used to solve robotic manipulation problems that would otherwise be infeasible without expert demonstrations. Source code is available at https://github.com/ondrejba/action_priors.

Action Priors for Large Action Spaces in Robotics

Action advising is a peer-to-peer knowledge exchange technique built on the teacher-student paradigm to alleviate the sample inefficiency problem in deep reinforcement learning. Recently proposed student-initiated approaches have obtained promising results. However, due to being in the early stages of development, these also have some substantial shortcomings. One of the abilities that are absent in the current methods is further utilising advice by reusing, which is especially crucial in the practical settings considering the budget constraints in peer-to-peer interactions. In this study, we present an approach to enable the student agent to imitate previously acquired advice to reuse them directly in its exploration policy, without any interventions in the learning mechanism itself. In particular, we employ a behavioural cloning module to imitate the teacher policy and use dropout regularisation to have a notion of epistemic uncertainty to keep track of which state-advice pairs are actually collected. As the results of experiments we conducted in three Atari games show, advice reusing via imitation is indeed a feasible option in deep RL and our approach can successfully achieve this while significantly improving the learning performance, even when it is paired with a simple early advising heuristic.

Action Advising with Advice Imitation in Deep Reinforcement Learning

In a multi-agent system where agents provide quantifiable work for each other on a voluntary basis, reputation mechanisms are incorporated to induce cooperation. Hereby agents assign their peers numerical scores based on their reported transaction histories. In such systems, adversaries can launch an attack by creating fake identities called Sybils, who report counterfeit transactions among one another, with the aim of increasing their own scores in the eyes of others. This  paper provides new results about the Sybil-proofness of reputation mechanisms. We revisit the impossibility result of Seuken and Parkes (2011), who show that strongly-beneficial Sybil attacks cannot be prevented on reputation mechanisms satisfying three particular requirements. We prove that, under a more rigorous set of definitions of Sybil attack benefit, this result no longer holds. We characterise  properties under which reputation mechanisms are susceptible to strongly-beneficial Sybil attacks. Building on our results, we propose a minimal set of requirements for reputation mechanisms to achieve resistance to such attacks, which are stronger   than the results by Cheng and Friedman (2005), who show Sybil-proofness of certain asymmetric reputation mechanisms.

Achieving Sybil-Proofness in Distributed Work Systems

Evolutionary strategies (ES) and off-policy learning algorithms are two major workhorses of Reinforcement learning (RL): ES adopt a simple blackbox approach to optimization but it can be slightly more sample inefficient; off-policy learning is by design more sample efficient but the updates can be unstable. Motivated by their trade-offs, we propose CEM-ACER, a combination of Cross-entropy method, a standard ES algorithm, and Actor-critic with experience replay (ACER), an off-policy actor-critic algorithm. Our proposal relies on a key insight: off-policy algorithms provide a natural mechanism to efficiently evolve parameter populations as part of an ES algorithm. Across a wide range of benchmark control tasks, we show that CEM-ACER balances the strengths of CEM and ACER, leading to an algorithm that consistently outperforms its individual building blocks, as well as other competitive baseline algorithms.

Guiding Evolutionary Strategies with Off-Policy Actor-Critic

Recent reinforcement learning studies extensively explore the interplay between cooperative and competitive behaviour in mixed environments. Unlike cooperative environments where agents strive towards a common goal, mixed environments are notorious for the conflicts of selfish and social interests. As a consequence, purely rational agents often struggle to maintain cooperation. A prevalent approach to induce cooperative behaviour is to assign additional rewards based on other agents' well-being. However, this approach suffers from the issue of multi-agent credit assignment, which can hinder performance. This issue is efficiently alleviated in cooperative setting with such state-of-the-art algorithms as QMIX and COMA. Still, when applied to mixed environments, these algorithms may result in unfair allocation of rewards. We propose BAROCCO, an extension of these algorithms capable to balance individual and social incentives. The mechanism behind BAROCCO is to train two distinct but interwoven components that jointly affect agents' decisions. We experimentally confirm the advantages of BAROCCO.

RPPLNS: Pay-per-last-N-shares with a Randomised Twist

Next from AAMAS 2021

Diverse Auto-Curriculum is Critical for Successful Real-World Multiagent Learning Systems (Blue Sky Ideas Track)

Similar lecture

Promoting Fair Proposers, Fair Responders or Both? Cost-Efficient Interference in the Spatial Ultimatum Game