In comparison to single-agent reinforcement learning (RL), MARL poses many challenges, chief of which is scalability [49]. Even if each agent's local state/action spaces are small, the size of the global state/action space can be large, potentially exponentially large in the number of agents, which renders many RL algorithms such as -learning not applicable. A promising approach for addressing the scalability challenge that has received attention in recent years is to exploit application-specific structures, e.g., [16, 32, 35]. A particularly important example of such a structure is a networked structure, e.g., applications in multi-agent networked systems such as social networks [6, 24], communication networks [44, 52], queueing networks [31], and smart transportation networks [51]. In these networked systems, it is often possible to exploit static, local dependency structures [1, 14, 15, 29], e.g., the fact that agents only interact with a fixed set of neighboring agents throughout the game. This sort of dependency structure often leads to scalable, distributed algorithms for optimization and control [1, 14, 29], and has proven effective for designing scalable and distributed MARL algorithms, e.g.

Model-based planning is often thought to be necessary for deep, careful reasoning and generalization in artificial agents. While recent successes of model-based reinforcement learning (MBRL) with deep function approximation have strengthened this hypothesis, the resulting diversity of model-based methods has also made it difficult to track which components drive success and why. In this paper, we seek to disentangle the contributions of recent methods by focusing on three questions: (1) How does planning benefit MBRL agents? (2) Within planning, what choices drive performance? (3) To what extent does planning improve generalization? To answer these questions, we study the performance of MuZero (Schrittwieser et al., 2019), a state-of-the-art MBRL algorithm, under a number of interventions and ablations and across a wide range of environments including control tasks, Atari, and 9x9 Go. Our results suggest the following: (1) The primary benefit of planning is in driving policy learning. (2) Using shallow trees with simple Monte-Carlo rollouts is as performant as more complex methods, except in the most difficult reasoning tasks. (3) Planning alone is insufficient to drive strong generalization. These results indicate where and how to utilize planning in reinforcement learning settings, and highlight a number of open questions for future MBRL research.

On Demand services, such as a ride sharing [1], coordination of multiply robots [2], user serving in MIMO networks [3] etc utilize management strategies in order to improve customer quality of service (QoS) requirements. The problem of shared resource utilization is very common in wireless networks [4] and becoming more important with more devices connected because of development of IoT and 5G. Usually such systems have multiply concurrent users awaiting serving and fewer number of workers resources available, along with switching costs from serving user to user (like trip for taxi driver from drop off of one user to pick up point of the next one). Real world systems are dynamic in nature with cause and effect information not being given and system behavior and QoS only being observed. Previous works developed different algorithmic or classical scheduling methods, where QoS is maintained via algorithm using some sort of priority index, like Proportional Fair [5], [3] or MLWDF [6]. This work focuses on reinforced learning applied to general formulation of user scheduling problem. A Q-learning based method is presented for maximizing customer QoS and compared to analytical strategies. A Q-learning approach is shown to improve QoS up to TODO% compared to baseline scenarios.

Applications of intelligent reflecting surfaces (IRSs) in wireless networks have attracted significant attention recently. Most of the relevant literature is focused on the single cell setting where a single IRS is deployed, while static and perfect channel state information (CSI) is assumed. In this work, we develop a novel methodology for multi-IRS-assisted multi-cell networks in the uplink. We formulate the sum-rate maximization problem aiming to jointly optimize the IRS reflect beamformers, base station (BS) combiners, and user equipment (UE) transmit powers. In this optimization, we consider the scenario in which (i) channels are dynamic and (ii) only partial CSI is available at each BS; specifically, scalar effective channels of local UEs and some of the interfering UEs. In casting this as a sequential decision making problem, we propose a multi-agent deep reinforcement learning algorithm to solve it, where each BS acts as an independent agent in charge of tuning the local UEs transmit powers, the local IRS reflect beamformer, and its combiners. We introduce an efficient message passing scheme that requires limited information exchange among the neighboring BSs to cope with the non-stationarity caused by the coupling of actions taken by multiple BSs. Our numerical simulations show that our method obtains substantial improvement in average data rate compared to several baseline approaches, e.g., fixed UEs transmit power and maximum ratio combining.

It is not every day that humans are exposed to questions like what will happen if technology exceeds the human thought process. Or what will happen if machines became conscious or start having conscience so that they can take decisions, equivalent to that of humans? However, scientists and researchers are looking out for an alternative solution that can perform tasks which the traditional artificial intelligence and its subsidiaries cannot. Termed as Artificial General Intelligence, this cutting edge technology has been acknowledged by scientists and researchers, since the inception of artificial intelligence. Artificial general intelligence will be the technology that pairs its general intelligence with deep reinforcement learning.

In this article, we will try to understand where On-Policy learning, Off-policy learning and offline learning algorithms fundamentally differ. Though there is a fair amount of intimidating jargon in reinforcement learning theory, these are just based on simple ideas. Reinforcement Learning is a subfield of machine learning that teaches an agent how to choose an action from its action space. It interacts with an environment, in order to maximize rewards over time. Complex enough? let's break this definition for better understanding.

One of the main challenges in imitation learning is determining what action an agent should take when outside the state distribution of the demonstrations. Inverse reinforcement learning (IRL) can enable generalization to new states by learning a parameterized reward function, but these approaches still face uncertainty over the true reward function and corresponding optimal policy. Existing safe imitation learning approaches based on IRL deal with this uncertainty using a maxmin framework that optimizes a policy under the assumption of an adversarial reward function, whereas risk-neutral IRL approaches either optimize a policy for the mean or MAP reward function. While completely ignoring risk can lead to overly aggressive and unsafe policies, optimizing in a fully adversarial sense is also problematic as it can lead to overly conservative policies that perform poorly in practice. To provide a bridge between these two extremes, we propose Bayesian Robust Optimization for Imitation Learning (BROIL). BROIL leverages Bayesian reward function inference and a user specific risk tolerance to efficiently optimize a robust policy that balances expected return and conditional value at risk. Our empirical results show that BROIL provides a natural way to interpolate between return-maximizing and risk-minimizing behaviors and outperforms existing risk-sensitive and risk-neutral inverse reinforcement learning algorithms. Code is available at https://github.com/dsbrown1331/broil.

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Target-driven visual navigation is a challenging problem that requires a robot to find the goal using only visual inputs. Many researchers have demonstrated promising results using deep reinforcement learning (deep RL) on various robotic platforms, but typical end-to-end learning is known for its poor extrapolation capability to new scenarios. Therefore, learning a navigation policy for a new robot with a new sensor configuration or a new target still remains a challenging problem. In this paper, we introduce a learning algorithm that enables rapid adaptation to new sensor configurations or target objects with a few shots. We design a policy architecture with latent features between perception and inference networks and quickly adapt the perception network via meta-learning while freezing the inference network. Our experiments show that our algorithm adapts the learned navigation policy with only three shots for unseen situations with different sensor configurations or different target colors. We also analyze the proposed algorithm by investigating various hyperparameters.

Learning models of the environment from data is often viewed as an essential component to building intelligent reinforcement learning (RL) agents. The common practice is to separate the learning of the model from its use, by constructing a model of the environment's dynamics that correctly predicts the observed state transitions. In this paper we argue that the limited representational resources of model-based RL agents are better used to build models that are directly useful for value-based planning. As our main contribution, we introduce the principle of value equivalence: two models are value equivalent with respect to a set of functions and policies if they yield the same Bellman updates. We propose a formulation of the model learning problem based on the value equivalence principle and analyze how the set of feasible solutions is impacted by the choice of policies and functions. Specifically, we show that, as we augment the set of policies and functions considered, the class of value equivalent models shrinks, until eventually collapsing to a single point corresponding to a model that perfectly describes the environment. In many problems, directly modelling state-to-state transitions may be both difficult and unnecessary. By leveraging the value-equivalence principle one may find simpler models without compromising performance, saving computation and memory. We illustrate the benefits of value-equivalent model learning with experiments comparing it against more traditional counterparts like maximum likelihood estimation. More generally, we argue that the principle of value equivalence underlies a number of recent empirical successes in RL, such as Value Iteration Networks, the Predictron, Value Prediction Networks, TreeQN, and MuZero, and provides a first theoretical underpinning of those results.