This paper studies the online reward poisoning problem, wherein an adversary deliberately manipulates the reward function during training to mislead the learning agent into adopting a mischievous policy. While the majority of existing reward poisoning research focuses on offline attacks, which assume prior knowledge of the transition probability, our work explores a more practical yet challenging dynamics-agnostic scenario. Specifically, we consider the scenario where the adversary has access to the agent’s replay buffer and can modify the reward data without the knowledge of transition probabilities. We formalize the poisoning task as an optimization problem and employ a reformulation method to circumvent the double-sampling issue. The proposed algorithm is provably convergent in the tabular setting and can be extended to the function approximation setting, where the poisoned reward network and the poisoned Q-value network are jointly learned to solve the problem. The algorithm’s effectiveness is validated through four distinct experimental evaluations.

Playing table tennis with a human player is a challenging robotic task due to its dynamic nature. Despite a number of researches being devoted to developing robotic table tennis systems, most of the works have demanding hardware requirements and ignore safety measures when generating the swing stoke. To address these issues, we propose a safe motion planning framework that fully pushes the robotic hardware performance limits to play table tennis. In particular, we propose a pipeline to generate manipulator joint trajectories with environmental safety constraints and scale the trajectories to satisfy joint movement limitations. We use three different agents to validate the planning algorithm with our handmade robot platform in both simulation and real-world environments.

Both goal-agnostic and goal-oriented tasks have practical value for robotic grasping: goal-agnostic tasks target all objects in the workspace, while goal-oriented tasks aim at grasping pre-assigned goal objects. However, most current grasping methods are only better at coping with one task. In this work, we propose a bifunctional push-grasping synergistic strategy for goal-agnostic and goal-oriented grasping tasks. Our method integrates pushing along with grasping to pick up all objects or pre-assigned goal objects with high action efficiency depending on the task requirement. We introduce a bifunctional network, which takes in visual observations and outputs dense pixel-wise maps of$Q$values for pushing and grasping primitive actions, to increase the available samples in the action space. Then we propose a hierarchical reinforcement learning framework to coordinate the two tasks by considering the goal-agnostic task as a combination of multiple goal-oriented tasks. To reduce the training difficulty of the hierarchical framework, we design a two-stage training method to train the two types of tasks separately. We perform pre-training of the model in simulation, and then transfer the learned model to the real world without any additional real-world fine-tuning. Experimental results show that the proposed approach outperforms existing methods in task completion rate and grasp success rate with less motion number. Supplementary material is available at https://github.com/DafaRen/Learning_Bifunctional_Push-grasping_Synergistic_Strategy_for_Goal-agnostic_and_Goal-oriented_Tasks.

The REINFORCE algorithm \cite{williams1992simple} is popular in policy gradient (PG) for solving reinforcement learning (RL) problems. Meanwhile, the theoretical form of PG is from \cite{sutton1999policy}. Although both formulae prescribe PG, their precise connections are not yet illustrated. Recently, \citeauthor{nota2020policy} (\citeyear{nota2020policy}) have found that the ambiguity causes implementation errors. Motivated by the ambiguity and implementation incorrectness, we study PG from a perturbation perspective. In particular, we derive PG in a unified framework, precisely clarify the relation between PG implementation and theory, and echos back the findings by \citeauthor{nota2020policy}. Diving into factors contributing to empirical successes of the existing erroneous implementations, we find that small approximation error and the experience replay mechanism play critical roles.

Deep anomaly detection has become popular for its capability of handling complex data. However, training a deep detector is fragile to data contamination due to overfitting. In this work, we study the performance of the anomaly detectors under data contamination and construct a data-efficient countermeasure against data contamination. We show that training a deep anomaly detector induces an implicit kernel machine. We then derive an information-theoretic bound of performance degradation with respect to the data contamination ratio. To mitigate the degradation, we propose a contradicting training approach. Apart from learning normality on the contaminated dataset, our approach discourages learning an additional small auxiliary dataset of labeled anomalies. Our approach is much more affordable than constructing a completely clean training dataset. Experiments on public datasets show that our approach significantly improves anomaly detection in the presence of contamination and outperforms some recently proposed detectors.

Flexible operation and maintenance (O&M) is critical for telecommunication (telecom) service providers due to the ever-growing communication networks. Currently, most O&M operations still rely on rule-based strategies, which only cover limited scenarios and are costly to extend for novel applications as expert knowledge is intensively involved. To build a more flexible O&M system, we propose a language model to extract useful representations out of massive network signaling messages and use the representations to perform downstream O&M tasks. Given that a vanilla language model is not directly applicable for the structured signaling messages, we develop an expert-knowledge-inspired statistical approach to preprocess the messages and a hierarchical network architecture to extract message relations among different levels. Moreover, network messages in the real world are often contaminated, which can mislead the language model to learn incorrect message patterns. To mitigate data contamination, we propose a reverse training method that prevents the language model from learning the contaminated data. We collected hundreds of thousands of signaling message flows to train the proposed signaling language model and applied the trained model to O&M tasks. Offline experiments show that our proposed language model captures various signaling protocols and the extracted representations enable us to achieve expert-level performance in network anomaly detection and service recognition. Our language model has been deployed online at Huawei and significantly improved O&M efficiency.

The restless multi-armed bandit (RMAB) problem is a generalization of the multi-armed bandit with non-stationary rewards. Its optimal solution is intractable due to exponentially large state and action spaces with respect to the number of arms. Existing approximation approaches, e.g., Whittle's index policy, have difficulty in capturing either temporal or spatial factors such as impacts from other arms. We propose considering both factors using the attention mechanism, which has achieved great success in deep learning. Our state-aware value function approximation solution comprises an attention-based value function approximator and a Bellman equation solver. The attention-based coordination module capture both spatial and temporal factors for arm coordination. The Bellman equation solver utilizes the decoupling structure of RMABs to acquire solutions with significantly reduced computation overheads. In particular, the time complexity of our approximation is linear in the number of arms. Finally, we illustrate the effectiveness and investigate the properties of our proposed method with numerical experiments.

Queue overflow of a dynamic queue system gives rise to the information loss (or packet loss) in the communication buffer or the decrease of throughput in the transportation network. This paper investigates a stochastic optimal control problem for dynamic queue systems when imposing probability constraints on queue overflows. We reformulate this problem as a Markov decision process (MDP) with safety constraints. We prove that both finite-horizon and infinite-horizon stochastic optimal control for MDP with such constraints can be transformed as a linear program (LP), respectively. Feasibility conditions are provided for the finite-horizon constrained control problem. Two implementation algorithms are designed under the assumption that only the state (not the state distribution) can be observed at each time instant. Simulation results compare optimal cost and state distribution among different scenarios, and show the probability constraint satisfaction by the proposed algorithms.