Hierarchical Reinforcement Learning with Opponent Modeling for Distributed Multi-agent Cooperation

Reinforcement learning is a mathematical framework for agents to interact intelligently with their environment. Unlike supervised learning, where a system learns with the help of labeled data, reinforcement learning agents learn how to act by trial and error only receiving a reward signal from their environments. A field where reinforcement learning has been prominently successful is robotics [3]. However, real-world control problems are also particularly challenging because of the noise and high- dimensionality of input data (e.g., visual input). In recent years, in the field of supervised learning, deep neural networks have been successfully used to extract meaning from this kind of data. Building on these advances, deep reinforcement learning was used to solve complex problems like Atari games and Go. Mnih et al. [1] built a system with fixed hyper parameters able to learn to play 49 different Atari games only from raw pixel inputs. However, in order to apply the same methods to real-world control problems, deep reinforcement learning has to be able to deal with continuous action spaces. Discretizing continuous action spaces would scale poorly, since the number of discrete actions grows exponentially with the dimensionality of the action. Furthermore, having a parametrized policy can be advantageous because it can generalize in the action space. Therefore with this thesis we study state-of-the-art deep reinforcement learning algorithm, Deep Deterministic Policy Gradients. We provide a theoretical comparison to other popular methods, an evaluation of its performance, identify its limitations and investigate future directions of research. The remainder of the thesis is organized as follows. We start by introducing the field of interest, machine learning, focusing our attention of deep learning and reinforcement learning. We continue by describing in details the two main algorithms, core of this study, namely Deep Q-Network (DQN) and Deep Deterministic Policy Gradients (DDPG). We then provide implementatory details of DDPG and our test environment, followed by a description of benchmark test cases. Finally, we discuss the results of our evaluation, identifying limitations of the current approach and proposing future avenues of research.

Energy efficient task scheduling based on deep reinforcement learning in cloud environment: A specialized review

A data-driven output voltage control of solid oxide fuel cell using multi-agent deep reinforcement learning

This paper presents a hierarchical deep reinforcement learning (DRL) method for the scheduling of energy consumptions of smart home appliances and distributed energy resources (DERs) including an energy storage system (ESS) and an electric vehicle (EV). Compared to Q-learning algorithms based on a discrete action space, the novelty of the proposed approach is that the energy consumptions of home appliances and DERs are scheduled in a continuous action space using an actor–critic-based DRL method. To this end, a two-level DRL framework is proposed where home appliances are scheduled at the first level according to the consumer’s preferred appliance scheduling and comfort level, while the charging and discharging schedules of ESS and EV are calculated at the second level using the optimal solution from the first level along with the consumer environmental characteristics. A simulation study is performed in a single home with an air conditioner, a washing machine, a rooftop solar photovoltaic system, an ESS, and an EV under a time-of-use pricing. Numerical examples under different weather conditions, weekday/weekend, and driving patterns of the EV confirm the effectiveness of the proposed approach in terms of total cost of electricity, state of energy of the ESS and EV, and consumer preference.

An optimal solutions-guided deep reinforcement learning approach for online energy storage control

With the improvement in the intelligence level of UAVs and the development of cluster control technology, the intelligent decision-making method of UAV cluster confrontation will become the key technology of UAV combat in the future. The UAV cluster confrontation learning environment is of complex characteristics of high dimension, non-linearity, incomplete information, continuous action space, and so on. Recently, artificial intelligence technology represented by deep learning and reinforcement learning has made a great breakthrough. Deep reinforcement learning has shown a great ability to solve intelligent decision-making problems in complex environments. In this paper, enlightened by the multi-agent centralized training distributed execution framework and the idea of maximum policy entropy, we propose a deep reinforcement learning method of the multi-agent soft actor-critic (MASAC) with incomplete information. A game model for UAV cluster confrontation based on multi-agent deep reinforcement learning is established, and a continuous space multiple UAV combat environment is constructed. Simulation experiments are performed on the asymmetric confrontation of UAV clusters with red and blue teams. The experimental results show that MASAC outperforms the existing popular multi-agent deep reinforcement learning methods, making the game players converge to a higher return equilibrium point of the game. Moreover, the convergence of MASAC is investigated and analyzed extensively, and the results show that MASAC is of good convergence and stability, ensuring the practicability of MASAC in intelligent decision-making regarding UAV cluster confrontation.

Multi-agent quantum-inspired deep reinforcement learning for real-time distributed generation control of 100% renewable energy systems

Autonomous control in high-dimensional, continuous state spaces is a persistent and important challenge in the fields of robotics and artificial intelligence. Because of high risk and complexity, the adoption of AI for autonomous combat systems has been a long-standing difficulty. In order to address these issues, DARPA's AlphaDogfight Trials (ADT) program sought to vet the feasibility of and increase trust in AI for autonomously piloting an F-16 in simulated air-to-air combat. Our submission to ADT solves the high-dimensional, continuous control problem using a novel hierarchical deep reinforcement learning approach consisting of a high-level policy selector and a set of separately trained low-level policies specialized for excelling in specific regions of the state space. Both levels of the hierarchy are trained using off-policy, maximum entropy methods with expert knowledge integrated through reward shaping. Our approach outperformed human expert pilots and achieved a second-place rank in the ADT championship event. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Impact Statement–</i> Significant performance milestones in reinforcement learning have been achieved in recent years, with autonomous agents demonstrating super-human performance across a wide variety of tasks. Before these algorithms can be extensively deployed in real-world defense applications, a greater level of trust must first be achieved. ADT was an important step towards developing the trust necessary to operationalize these algorithms, by demonstrating their effectiveness on a foundational yet relevant problem in a high-fidelity simulation environment. Developed for the program, our hierarchical reinforcement learning agent was designed alongside of and competed against active fighter pilots, and ultimately defeated a graduate of the United States Air Force's F-16 Weapons Instructor Course in match play.

Representation learning for continuous action spaces is beneficial for efficient policy learning

The growing number of complex and heterogeneous Internet of Things (IoT) applications has imposed a high demand for scarce communications and computing resources. To meet this stringent requirement, it is desirable to develop large-scale highly adaptive online resource allocation strategies to streamline existing network operations. Deep reinforcement learning (DRL), which combines the merits of reinforcement learning and deep learning, is capable of addressing complex decision-making tasks, thus enabling efficient online resource allocation. In this article, we present a DRL-based resource allocation framework. We begin a discussion on DRL basics and review its several recent applications. Then, we develop two new DRL algorithms that facilitate unlocking the potential of DRL and offer viable solutions to many more complex resource allocation problems. The first one tackles an optimization problem exposed to mixed (discrete and continuous) action spaces and bound by a number of highly non-linear quality-of-service (QoS) constraints. The second one extends the single-agent DRL to a more challenging multi-agent DRL by introducing a novel semi-distributed architecture. Finally, we discuss the challenges and future visions of applying DRL to real-world IoT networks.

Deep reinforcement learning (DRL) has become a popular learning paradigm for decision and control and has been widely applied in robot manipulation in recent years. However, due to its special learning pattern of “trial and error”, there are still some remaining problems with DRL. Such as exploration dilemma, sample inefficient, and slow convergence, are to be refined, especially when faced with complex long-horizon tasks. As a solution for these limits, hierarchical reinforcement learning (HRL) is proposed and developed by decomposing challenging tasks into multiple simpler subtasks, to efficiently solve the main task in a“divide and conquer” manner. At present, there are comprehensive HRL methods for robotic manipulation tasks, while a review is lacking. To facilitate researchers to form a general view of this field, we systematically summarize related HRL methods for robotic manipulation. This review carries out literature sortation in a novel taxonomy of subtask generation and divides HRL methods into two categories: handcrafted subtask generation and learning-based subtask generation. A great number of representative methods are analyzed in detail. In the end, we also present some important future directions for HRL.

Optimal scheduling of island integrated energy systems considering multi-uncertainties and hydrothermal simultaneous transmission: A deep reinforcement learning approach

Fixed-Wing UAVs flocking in continuous spaces: A deep reinforcement learning approach

The modern network is equipped with many service functions to acquire high-quality service. The emergence of network function virtualization (NFV) provides a convenient way to accomplish the network services in the form of virtual network function (VNF) and also makes the scheduling solution of VNFs flexible. The VNFs can be deployed on commodity servers as software processes. Besides, multiple VNFs are chained in a specified order as a service function chain (SFC) to serve a given flow, increasing the scheduling difficulty to minimize the average flow completion time. In this paper, we study the problem of scheduling multiple SFCs with the constraint of different resource limitations in various commodity servers. This problem is typically formulated as an Integer Linear Programming (ILP) problem, which is NP-hard. To well tackle this problem, we propose a deep reinforcement learning (DRL) approach. It involves multi-step decision making, which can be naturally transformed into a DRL problem. We design specific reward and state representations for such a multi-resource scheduling problem. We also consider how to use DRL to handle online requests of SFCs. The experiment results demonstrate that the DRL approach can significantly reduce the average completion time of a set of SFC and achieves a cost saving of 39.94% against the benchmark method.

In this study we investigate the potential of using Deep Reinforcement Learning (DRL) to day trade stocks, taking into account the constraints imposed by the stock market, such as liquidity, latency, slippage and transaction costs. More specifically, we use a Deep Deterministic Policy Gradient (DDPG) algorithm to solve a series of asset allocation problems in order to define the percentage of capital that must be invested in each asset at each period, executing exclusively day trade operations. DDPG is a model-free, off-policy actor-critic method that can learn policies in high-dimensional and continuous action and state spaces, like the ones normally found in financial market environments. The proposed day trading system was tested in B3 - Brazil Stock Exchange, an important and understudied market, especially considering the application of DRL techniques to alpha generation. A series of experiments were performed from the beginning of 2017 until the end of 2019 and compared with ten benchmarks, including Ibovespa, the most important Brazilian market index, and the stock portfolios suggested by the main Brazilian banks and brokers during these years. The results were evaluated considering return and risk metrics and showed that the proposed method outperformed the benchmarks by a huge margin. The best results obtained by the algorithm had a cumulative percentage return of 311% in three years, with an annual average maximum drawdown around 19%.