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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.

AAMAS 2021

Self-Imitation Advantage Learning

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
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AAAI 2023

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

technical paper

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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

Recent advances in the blockchain research have been made in two important directions. One is refined resilience analysis utilizing game theory to study the consequences of selfish behavior of users (miners), and the other is the extension from a linear (chain) structure to a non-linear (graphical) structure for performance improvements, such as IOTA and Graphcoin. The first question that comes to mind is ? what improvements that a blockchain system would see by leveraging these new advances. In this paper, we consider three major properties for a blockchain system: ?-partial verification, scalability, and finality-duration. We establish a formal framework and prove that no blockchain system can achieve ?-partial verification for any fixed constant ?, high scalability, and low finality-duration simultaneously. We observe that classical blockchain systems like Bitcoin achieves full verification (?=1) and low finality-duration, Ethereum 2.0 Sharding achieves low finality-duration and high scalability. We are interested in whether it is possible to partially satisfy the three properties.

A Game Theoretical Analysis of Non-Linear Blockchain System

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

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.

Self-Imitation Advantage Learning

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Learning to Cooperate with Unseen Agents Through Meta-Reinforcement Learning

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Self-Imitation Advantage Learning

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Next from AAMAS 2021

Learning to Cooperate with Unseen Agents Through Meta-Reinforcement Learning

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Learning Adversarially Robust Sparse Networks via Weight Reparameterization

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