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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&#39;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.

AAMAS 2021

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

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).  

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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.

poster

Ad hoc teamwork problem describes situations where an agent has to cooperate with previously unseen agents to achieve a common goal. For an agent to be successful in these scenarios, it has to have cooperative skills. One could implement cooperative skills into an agent by using domain knowledge (e.g., goals, roles, and protocols) to design the agent's behaviours. However, in complex domains, domain knowledge might not be available. Therefore, it is interesting to explore how to directly learn cooperative skills from data. In this work, we apply meta-reinforcement learning (meta-RL) formulation in the context of ad hoc teamwork problem. Our experiments show that such a method could produce cooperative agents in two cooperative environments with different cooperative circumstances.

Learning to Cooperate with Unseen Agents Through Meta-Reinforcement Learning

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

Thompson sampling is a well-known approach for balancing exploration and exploitation in reinforcement learning. It requires the posterior distribution of value-action functions to be maintained; this is generally intractable for tasks that have a high dimensional state-action space. We derive a variational Thompson sampling approximation for DQNs which uses a deep network whose parameters are perturbed by a learned variational noise distribution. We interpret the successful NoisyNets method \citefortunato2018noisy as an approximation to the variational Thompson sampling method that we derive. Further, we propose State Aware Noisy Exploration (SANE) which seeks to improve on NoisyNets by allowing a non-uniform perturbation, where the amount of parameter perturbation is conditioned on the state of the agent. This is done with the help of an auxiliary perturbation module, whose output is state dependent and is learnt end to end with gradient descent. We hypothesize that such state-aware noisy exploration is particularly useful in problems where exploration in certain high risk states may result in the agent failing badly. We demonstrate the effectiveness of the state-aware exploration method in the off-policy setting by augmenting DQNs with the auxiliary perturbation module.

State-Aware Variational Thompson Sampling for Deep Q-Networks

In stochastic games, individuals need to make decisions in multiple states and transitions between states influence the dynamics of strategies significantly. In this work, by describing the dynamic process in stochastic game as a Markov chain and utilizing the transition matrix, we introduce a new method, named state-transition replicator dynamics, to obtain the replicator dynamics of a stochastic game. Based on our proposed model, we can gain qualitative and detailed insights into the influence of transition probabilities on the dynamics of strategies. We illustrate that a set of unbalanced transition probabilities can help players to overcome the social dilemmas and lead to mutual cooperation in a cooperation back state, even if the stochastic game has the same social dilemmas in each state. Moreover, we also present that a set of specifically designed transition probabilities can fix the expected payoffs of one player and make him lose the motivation to update his strategies in the stochastic game.

Modeling Replicator Dynamics in Stochastic Games Using Markov Chain Method

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

In many risk-aware and multi-objective reinforcement learning settings, the utility of the user is derived from the single execution of a policy. In these settings, making decisions based on the average future returns is not suitable. For example, in a medical setting a patient may only have one opportunity to treat their illness. When making a decision, just the expected return -- known in reinforcement learning as the value -- cannot account for the potential range of adverse or positive outcomes a decision may have. Our key insight is that we should use the distribution over expected future returns differently to represent the critical information that the agent requires at decision time.

Distributional Monte Carlo Tree Search for Risk-Aware and Multi-Objective Reinforcement Learning

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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