No Arabic abstract
As online shopping prevails and e-commerce platforms emerge, there is a tremendous number of parcels being transported every day. Thus, it is crucial for the logistics industry on how to assign a candidate logistics route for each shipping parcel properly as it leaves a significant impact on the total logistics cost optimization and business constraints satisfaction such as transit hub capacity and delivery proportion of delivery providers. This online route-assignment problem can be viewed as a constrained online decision-making problem. Notably, the large amount (beyond ${10^5}$) of daily parcels, the variability and non-Markovian characteristics of parcel information impose difficulties on attaining (near-) optimal solution without violating constraints excessively. In this paper, we develop a model-free DRL approach named PPO-RA, in which Proximal Policy Optimization (PPO) is improved with dedicated techniques to address the challenges for route assignment (RA). The actor and critic networks use attention mechanism and parameter sharing to accommodate each incoming parcel with varying numbers and identities of candidate routes, without modeling non-Markovian parcel arriving dynamics since we make assumption of i.i.d. parcel arrival. We use recorded delivery parcel data to evaluate the performance of PPO-RA by comparing it with widely-used baselines via simulation. The results show the capability of the proposed approach to achieve considerable cost savings while satisfying most constraints.
Although well-established in general reinforcement learning (RL), value-based methods are rarely explored in constrained RL (CRL) for their incapability of finding policies that can randomize among multiple actions. To apply value-based methods to CRL, a recent groundbreaking line of game-theoretic approaches uses the mixed policy that randomizes among a set of carefully generated policies to converge to the desired constraint-satisfying policy. However, these approaches require storing a large set of policies, which is not policy efficient, and may incur prohibitive memory costs in constrained deep RL. To address this problem, we propose an alternative approach. Our approach first reformulates the CRL to an equivalent distance optimization problem. With a specially designed linear optimization oracle, we derive a meta-algorithm that solves it using any off-the-shelf RL algorithm and any conditional gradient (CG) type algorithm as subroutines. We then propose a new variant of the CG-type algorithm, which generalizes the minimum norm point (MNP) method. The proposed method matches the convergence rate of the existing game-theoretic approaches and achieves the worst-case optimal policy efficiency. The experiments on a navigation task show that our method reduces the memory costs by an order of magnitude, and meanwhile achieves better performance, demonstrating both its effectiveness and efficiency.
The ability to transfer knowledge to novel environments and tasks is a sensible desiderata for general learning agents. Despite the apparent promises, transfer in RL is still an open and little exploited research area. In this paper, we take a brand-new perspective about transfer: we suggest that the ability to assign credit unveils structural invariants in the tasks that can be transferred to make RL more sample-efficient. Our main contribution is SECRET, a novel approach to transfer learning for RL that uses a backward-view credit assignment mechanism based on a self-attentive architecture. Two aspects are key to its generality: it learns to assign credit as a separate offline supervised process and exclusively modifies the reward function. Consequently, it can be supplemented by transfer methods that do not modify the reward function and it can be plugged on top of any RL algorithm.
Inverse reinforcement learning (IRL) is the problem of learning the preferences of an agent from the observations of its behavior on a task. While this problem has been well investigated, the related problem of {em online} IRL---where the observations are incrementally accrued, yet the demands of the application often prohibit a full rerun of an IRL method---has received relatively less attention. We introduce the first formal framework for online IRL, called incremental IRL (I2RL), and a new method that advances maximum entropy IRL with hidden variables, to this setting. Our formal analysis shows that the new method has a monotonically improving performance with more demonstration data, as well as probabilistically bounded error, both under full and partial observability. Experiments in a simulated robotic application of penetrating a continuous patrol under occlusion shows the relatively improved performance and speed up of the new method and validates the utility of online IRL.
Deep reinforcement learning enables an agent to capture users interest through interactions with the environment dynamically. It has attracted great interest in the recommendation research. Deep reinforcement learning uses a reward function to learn users interest and to control the learning process. However, most reward functions are manually designed; they are either unrealistic or imprecise to reflect the high variety, dimensionality, and non-linearity properties of the recommendation problem. That makes it difficult for the agent to learn an optimal policy to generate the most satisfactory recommendations. To address the above issue, we propose a novel generative inverse reinforcement learning approach, namely InvRec, which extracts the reward function from users behaviors automatically, for online recommendation. We conduct experiments on an online platform, VirtualTB, and compare with several state-of-the-art methods to demonstrate the feasibility and effectiveness of our proposed approach.
Reward decomposition is a critical problem in centralized training with decentralized execution~(CTDE) paradigm for multi-agent reinforcement learning. To take full advantage of global information, which exploits the states from all agents and the related environment for decomposing Q values into individual credits, we propose a general meta-learning-based Mixing Network with Meta Policy Gradient~(MNMPG) framework to distill the global hierarchy for delicate reward decomposition. The excitation signal for learning global hierarchy is deduced from the episode reward difference between before and after exercise updates through the utility network. Our method is generally applicable to the CTDE method using a monotonic mixing network. Experiments on the StarCraft II micromanagement benchmark demonstrate that our method just with a simple utility network is able to outperform the current state-of-the-art MARL algorithms on 4 of 5 super hard scenarios. Better performance can be further achieved when combined with a role-based utility network.