United Kingdom

We model interpretive blindness, a type of epistemic bias that poses a problem for learning from testimony, in which one acquires information from text or conversation but lacks direct access to ground truth. Interpretive blindness arises when a co-dependence between background beliefs and interpretation leads to a dynamic process of bias hardening that impedes or precludes learning. We argue that when bodies of data are argumentatively complete, even constraints from hierarchical Bayesian learning designed to promote good epistemic practices will fail to stop interpretive blindness.

AAMAS 2021

Interpretive Blindness and the Impossibility of Learning from Testimony

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poster

Deep Reinforcement Learning (RL) methods rely on experience replay to approximate the minibatched supervised learning setting; however, unlike supervised learning where access to lots of training data is crucial to generalization, replay-based deep RL appears to struggle in the presence of extraneous data. Recent works have shown that the performance of Deep Q-Network (DQN) degrades when its replay memory becomes too large.

Stratified Experience Replay: Correcting Multiplicity Bias in Off-Policy Reinforcement Learning

We study the problem of learning helpful behavior, specifically, learning to cooperate with differently-skilled and diverse partners in the context of two-player, cooperative Atari games. We show robust performance of these so-called Helper-AIs when paired with different kinds of partners (both human and artificial agents), including partners that they have not previously encountered during training. In particular, while pairing an expert AI with a non-expert AI leads to performance that is worse than when pairing the non-expert AI with a copy of itself, these Helper-AIs provide a substantial boost in joint performance.

Learning Robust Helpful Behaviors in Two-Player Cooperative Atari Environments

Recent impossibility results have shown that strategyproofness is difficult to obtain for multiwinner voting rules, especially in combination with proportionality. In this paper, we attempt to identify cases where strategyproofness can be established by considering manipulation on party-list profiles. We distinguish between three types of manipulation  subset-manipulation, superset-manipulation, and disjoint-set-manipulation. Our focus is the class of irresolute Thiele rules. For all three types of manipulation, we are able to establish that Thiele rules are strategyproof on party-list profiles for several well-known preference extensions. For superset- and disjoint-set-strategyproofness, we can extend this result to all preference extensions. We are also able to show that Thiele rules are fully strategyproof for optimistic agents on these profiles.

Manipulability of Thiele Methods on Party-List Profiles

Egalitarian considerations play a central role in many areas of social choice theory. Applications of egalitarian principles range from ensuring everyone gets an equal share of a cake when deciding how to divide it, to guaranteeing balance with respect to gender or ethnicity in committee elections. Yet, the egalitarian approach has received little attention in judgment aggregation  a powerful framework for aggregating logically interconnected issues. We make the first steps towards filling that gap. We introduce axioms capturing two classical interpretations of egalitarianism in judgment aggregation and situate these within the context of existing axioms in the pertinent framework of belief merging. We then explore the relationship between these axioms and several notions of strategyproofness from social choice theory at large. Finally, a novel egalitarian judgment aggregation rule stems from our analysis; we present complexity results concerning both outcome determination and strategic manipulation for that rule.

Egalitarian Judgment Aggregation

Self-imitation learning is a Reinforcement Learning (RL) method that encourages actions whose returns were higher than expected, which helps in hard exploration and sparse reward problems. It was shown to improve the performance of on-policy actor-critic methods in several discrete control tasks. Nevertheless, applying self-imitation to the mostly action-value based off-policy RL methods is not straightforward. We propose SAIL, a novel generalization of self-imitation learning for off-policy RL, based on a modification of the Bellman optimality operator that we connect to Advantage Learning. Crucially, our method mitigates the problem of stale returns by choosing the most optimistic return estimate between the observed return and the current action-value for self-imitation. We demonstrate the empirical effectiveness of SAIL on the Arcade Learning Environment, with a focus on hard exploration games.

Self-Imitation Advantage Learning

Deploying reinforcement learning (RL) involves major concerns around safety. Engineering a reward signal that allows the agent to maximize its performance while remaining safe is not trivial. Safe RL studies how to mitigate such problems. For instance, we can decouple safety from reward using constrained Markov decision processes (CMDPs), where an independent signal models the safety aspects. In this setting, an RL agent can autonomously find tradeoffs between performance and safety. Unfortunately, most RL agents designed for CMDPs only guarantee safety after the learning phase, which might prevent their direct deployment. In this work, we investigate settings where a concise abstract model of the safety aspects is given, a reasonable assumption since a thorough understanding of safety-related matters is a prerequisite for deploying RL in typical applications. Factored CMDPs provide such compact models when a small subset of features describe the dynamics relevant for the safety constraints. We propose an RL algorithm that uses this abstract model to learn policies for CMDPs safely, that is without violating the constraints. During the training process, this algorithm can seamlessly switch from a conservative policy to a greedy policy without violating the safety constraints. We prove that this algorithm is safe under the given assumptions. Empirically, we show that even if safety and reward signals are contradictory, this algorithm always operates safely and, when they are aligned, this approach also improves the agent's performance.

AlwaysSafe: Reinforcement Learning without Safety Constraint Violations during Training

The proliferation of unmanned aerial vehicles (UAVs) has flourished various intelligent services, in which the effective coordination plays a significant role in enhancing swarm execution efficiency. However, due to the unreliable communication in the air as well as the heterogeneity in operation mode, it is challenging to achieve highly coordinated actions, particularly in the fully distributed environment with incomplete observations. In this paper, we leverage the generative adversarial imitation learning (GAIL) technique to coordinate the drones' actions by directly imitating the peer's demonstrations. In order to characterize the true environment state under local incomplete observations, we transform historical observation-action trajectories into belief representations, which are trained in conjunction with the imitation policies. We also gain regularized belief representations by correlating the prediction of future states, the trace of historical contexts, and the action-assisted guidance information, which contribute to more accurate imitation policies. We evaluate the proposed algorithm on the drones' formation control scenario. Evaluation results show the superiorities on imitation accuracy, teamwork execution time and energy cost.

Drone Formation Control via Belief-Correlated Imitation Learning

We investigate a repeated two-player game setting where the column player is also a designer of the system, and has full control over payoff matrices. In addition, we assume that the row player uses a no-regret algorithm to efficiently learn how to adapt their strategy to the column player's behaviour over time. The goal of the column player is to guide her opponent into picking a mixed strategy which is preferred by the system designer. Therefore, she needs to: (i) design appropriate payoffs for both players; and (ii) strategically interact with the row player during a sequence of plays in order to guide her opponent to converge to the desired mixed strategy. To design appropriate payoffs, we propose a novel zero-sum game construction whose unique minimax solution contains the desired behaviour. We also propose another construction in which only the minimax strategy of the row player is unique. Finally, we propose a new game playing algorithm for the system designer and show that it can guide the row player to its minimax strategy, under the assumption that the row player adopts a stable  no-regret algorithm.

How to Guide a Non-Cooperative Learner to Cooperate: Exploiting No-Regret Algorithms in System Design

Competitive Multi-Agent Systems (MAS) are inherently hard to control due to agent autonomy and strategic behavior, which is particularly problematic when there are system-level objectives to be achieved or specific environmental states to be avoided.

Toward a Self-Learning Governance Loop for Competitive Multi-Attribute MAS

This paper studies distributed Q-learning for Linear Quadratic Regulator (LQR) in a multi-agent network. The existing results often assume that agents can observe the global system state, which may be infeasible in large-scale systems due to privacy concerns or communication constraints. In this work, we consider a setting with unknown system models and no centralized coordinator. We devise a state tracking (ST) based Q-learning algorithm to design optimal controllers for agents. Specifically, we assume that agents maintain local estimates of the global state based on their local information and communications with neighbors. At each step, every agent updates its local global state estimation, based on which it solves an approximate Q-factor locally through policy iteration. Assuming a decaying injected excitation noise during the policy evaluation, we prove that the local estimation converges to the true global state, and establish the convergence of the proposed distributed ST-based Q-learning algorithm. The experimental studies corroborate our theoretical results by showing that our proposed method achieves comparable performance with the centralized case.

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Stratified Experience Replay: Correcting Multiplicity Bias in Off-Policy Reinforcement Learning

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Interpretive Blindness and the Impossibility of Learning from Testimony