The skill to drift a car--i.e., operate in a state of controlled oversteer like professional drivers--could give future autonomous cars maximum flexibility when they need to retain control in adverse conditions or avoid collisions. We investigate real-time drifting strategies that put the car where needed while bypassing expensive trajectory optimization. To this end, we design a reinforcement learning agent that builds on the concept of tire energy absorption to autonomously drift through changing and complex waypoint configurations while safely staying within track bounds. We achieve zero-shot deployment on the car by training the agent in a simulation environment built on top of a neural stochastic differential equation vehicle model learned from pre-collected driving data. Experiments on a Toyota GR Supra and Lexus LC 500 show that the agent is capable of drifting smoothly through varying waypoint configurations with tracking error as low as 10 cm while stably pushing the vehicles to sideslip angles of up to 63{\deg}.

Recently, research on open domain dialogue systems have attracted extensive interests of academic and industrial researchers. The goal of an open domain dialogue system is to imitate humans in conversations. Previous works on single turn conversation generation have greatly promoted the research of open domain dialogue systems. However, understanding multiple single turn conversations is not equal to the understanding of multi turn dialogue due to the coherent and context dependent properties of human dialogue. Therefore, in open domain multi turn dialogue generation, it is essential to modeling the contextual semantics of the dialogue history, rather than only according to the last utterance. Previous research had verified the effectiveness of the hierarchical recurrent encoder-decoder framework on open domain multi turn dialogue generation. However, using RNN-based model to hierarchically encoding the utterances to obtain the representation of dialogue history still face the problem of a vanishing gradient. To address this issue, in this paper, we proposed a static and dynamic attention-based approach to model the dialogue history and then generate open domain multi turn dialogue responses. Experimental results on Ubuntu and Opensubtitles datasets verify the effectiveness of the proposed static and dynamic attention-based approach on automatic and human evaluation metrics in various experimental settings. Meanwhile, we also empirically verify the performance of combining the static and dynamic attentions on open domain multi turn dialogue generation.

The universe involves many independent co-learning agents as an ever-evolving part of our observed environment. Yet, in practice, Multi-Agent Reinforcement Learning (MARL) applications are usually constrained to small, homogeneous populations and remain computationally intensive. In this paper, we study how large heterogeneous populations of learning agents evolve in normal-form games. We show how, under assumptions commonly made in the multi-armed bandit literature, Multi-Agent Policy Gradient closely resembles the Replicator Dynamic, and we further derive a fast, parallelizable implementation of Opponent-Learning Awareness tailored for evolutionary simulations. This enables us to simulate the evolution of very large populations made of heterogeneous co-learning agents, under both naive and advanced learning strategies. We demonstrate our approach in simulations of 200,000 agents, evolving in the classic games of Hawk-Dove, Stag-Hunt, and Rock-Paper-Scissors. Each game highlights distinct ways in which Opponent-Learning Awareness affects evolution.

We assume accurate observation of battery state of charge (SOC) and precise speed curves when addressing the constrained optimal fuel consumption (COFC) problem via constrained reinforcement learning (CRL). However, in practice, SOC measurements are often distorted by noise or confidentiality protocols, and actual reference speeds may deviate from expectations. We aim to minimize fuel consumption while maintaining SOC balance under observational perturbations in SOC and speed. This work first worldwide uses seven training approaches to solve the COFC problem under five types of perturbations, including one based on a uniform distribution, one designed to maximize rewards, one aimed at maximizing costs, and one along with its improved version that seeks to decrease reward on Toyota Hybrid Systems (THS) under New European Driving Cycle (NEDC) condition. The result verifies that the six can successfully solve the COFC problem under observational perturbations, and we further compare the robustness and safety of these training approaches and analyze their impact on optimal fuel consumption.

Multimedia streaming accounts for the majority of traffic in today's internet. Mechanisms like adaptive bitrate streaming control the bitrate of a stream based on the estimated bandwidth, ideally resulting in smooth playback and a good Quality of Experience (QoE). However, selecting the optimal bitrate is challenging under volatile network conditions. This motivated researchers to train Reinforcement Learning (RL) agents for multimedia streaming. The considered training environments are often simplified, leading to promising results with limited applicability. Additionally, the QoE fairness across multiple streams is seldom considered by recent RL approaches. With this work, we propose a novel multi-agent environment that comprises multiple challenges of fair multimedia streaming: partial observability, multiple objectives, agent heterogeneity and asynchronicity. We provide and analyze baseline approaches across five different traffic classes to gain detailed insights into the behavior of the considered agents, and show that the commonly used Proximal Policy Optimization (PPO) algorithm is outperformed by a simple greedy heuristic. Future work includes the adaptation of multi-agent RL algorithms and further expansions of the environment.

Unmanned vehicles able to conduct advanced operations without human intervention are being developed at a fast pace for many purposes. Not surprisingly, they are also expected to significantly change how military operations can be conducted. To leverage the potential of this new technology in a physically and logically contested environment, security risks are to be assessed and managed accordingly. Research on this topic points to autonomous cyber defence as one of the capabilities that may be needed to accelerate the adoption of these vehicles for military purposes. Here, we pursue this line of investigation by exploring reinforcement learning to train an agent that can autonomously respond to cyber attacks on unmanned vehicles in the context of a military operation. We first developed a simple simulation environment to quickly prototype and test some proof-of-concept agents for an initial evaluation. This agent was then applied to a more realistic simulation environment and finally deployed on an actual unmanned ground vehicle for even more realism. A key contribution of our work is demonstrating that reinforcement learning is a viable approach to train an agent that can be used for autonomous cyber defence on a real unmanned ground vehicle, even when trained in a simple simulation environment.

Integration of human feedback plays a key role in improving the learning capabilities of intelligent systems. This comparative study delves into the performance, robustness, and limitations of imitation learning compared to traditional reinforcement learning methods within these systems. Recognizing the value of human-in-the-loop feedback, we investigate the influence of expert guidance and suboptimal demonstrations on the learning process. Through extensive experimentation and evaluations conducted in a pre-existing simulation environment using the Unity platform, we meticulously analyze the effectiveness and limitations of these learning approaches. The insights gained from this study contribute to the advancement of human-centered artificial intelligence by highlighting the benefits and challenges associated with the incorporation of human feedback into the learning process. Ultimately, this research promotes the development of models that can effectively address complex real-world problems.

Motivated by engineering applications such as resource allocation in networks and inventory systems, we consider average-reward Reinforcement Learning with unbounded state space and reward function. Recent works studied this problem in the actor-critic framework and established finite sample bounds assuming access to a critic with certain error guarantees. We complement their work by studying Temporal Difference (TD) learning with linear function approximation and establishing finite-time bounds with the optimal $\mathcal{O}\left(1/\epsilon^2\right)$ sample complexity. These results are obtained using the following general-purpose theorem for non-linear Stochastic Approximation (SA). Suppose that one constructs a Lyapunov function for a non-linear SA with certain drift condition. Then, our theorem establishes finite-time bounds when this SA is driven by unbounded Markovian noise under suitable conditions. It serves as a black box tool to generalize sample guarantees on SA from i.i.d. or martingale difference case to potentially unbounded Markovian noise. The generality and the mild assumption of the setup enables broad applicability of our theorem. We illustrate its power by studying two more systems: (i) We improve upon the finite-time bounds of $Q$-learning by tightening the error bounds and also allowing for a larger class of behavior policies. (ii) We establish the first ever finite-time bounds for distributed stochastic optimization of high-dimensional smooth strongly convex function using cyclic block coordinate descent.

We study the dynamic pricing and replenishment problems under inconsistent papers to INFORMS journals by means decision frequencies. Different from the traditional demand assumption, the of a style file template, which includes discreteness of demand and the parameter within the Poisson distribution as a function the journal title. However, use of a template of price introduce complexity into analyzing the problem property. We demonstrate does not certify that the paper the concavity of the single-period profit function with respect to product price and has been accepted for publication in the inventory within their respective domains. The demand model is enhanced by integrating named journal. INFORMS journal templates a decision tree-based machine learning approach, trained on comprehensive are for the exclusive purpose of market data. Employing a two-timescale stochastic approximation scheme, we address submitting to an INFORMS journal and the discrepancies in decision frequencies between pricing and replenishment, ensuring are not intended to be a true representation convergence to local optimum. We further refine our methodology by incorporating of the article's final published form.

We consider off-dynamics reinforcement learning (RL) where one needs to transfer policies across different domains with dynamics mismatch. Despite the focus on developing dynamics-aware algorithms, this field is hindered due to the lack of a standard benchmark. To bridge this gap, we introduce ODRL, the first benchmark tailored for evaluating off-dynamics RL methods. ODRL contains four experimental settings where the source and target domains can be either online or offline, and provides diverse tasks and a broad spectrum of dynamics shifts, making it a reliable platform to comprehensively evaluate the agent's adaptation ability to the target domain. Furthermore, ODRL includes recent off-dynamics RL algorithms in a unified framework and introduces some extra baselines for different settings, all implemented in a single-file manner. To unpack the true adaptation capability of existing methods, we conduct extensive benchmarking experiments, which show that no method has universal advantages across varied dynamics shifts. We hope this benchmark can serve as a cornerstone for future research endeavors.