Reinforcement-learning agents seek to maximize a reward signal through environmental interactions. As humans, our contribution to the learning process is through designing the reward function. Like programmers, we have a behavior in mind and have to translate it into a formal specification, namely rewards. In this work, we consider the reward-design problem in tasks formulated as reaching desirable states and avoiding undesirable states. To start, we propose a strict partial ordering of the policy space. We prefer policies that reach the good states faster and with higher probability while avoiding the bad states longer. Next, we propose an environment-independent tiered reward structure and show it is guaranteed to induce policies that are Pareto-optimal according to our preference relation. Finally, we empirically evaluate tiered reward functions on several environments and show they induce desired behavior and lead to fast learning.

Recent advances in reinforcement-learning research have demonstrated impressive results in building algorithms that can out-perform humans in complex tasks. Nevertheless, creating reinforcement-learning systems that can build abstractions of their experience to accelerate learning in new contexts still remains an active area of research. Previous work showed that reward-predictive state abstractions fulfill this goal, but have only be applied to tabular settings. Here, we provide a clustering algorithm that enables the application of such state abstractions to deep learning settings, providing compressed representations of an agent's inputs that preserve the ability to predict sequences of reward. A convergence theorem and simulations show that the resulting reward-predictive deep network maximally compresses the agent's inputs, significantly speeding up learning in high dimensional visual control tasks. Furthermore, we present different generalization experiments and analyze under which conditions a pre-trained reward-predictive representation network can be re-used without re-training to accelerate learning -- a form of systematic out-of-distribution transfer.

我们提出了一种新型的参数化技能学习算法，旨在学习可转移的参数化技能并将其合成为新的动作空间，以支持长期任务中的有效学习。我们首先提出了新颖的学习目标 - 以轨迹为中心的多样性和平稳性 - 允许代理商能够重复使用的参数化技能。我们的代理商可以使用这些学习的技能来构建时间扩展的参数化行动马尔可夫决策过程，我们为此提出了一种层次的参与者 - 批判算法，旨在通过学习技能有效地学习高级控制政策。我们从经验上证明，所提出的算法使代理能够解决复杂的长途障碍源环境。

我们采用了近端迭代，以便在加固学习中进行价值函数优化。近端迭代是一种计算上有效的技术，使我们能够向更理想的解决方案偏置优化过程。作为近端迭代在深增强学习中的具体应用，我们将深度Q-Network（DQN）代理具有近期术语的目标函数，以确保DQN的在线网络组件仍保留在目标网络附近。我们用近端迭代调用DQN或DQNPRO的所得代理，在ATARI基准测试中对原始DQN的显着改进。我们的结果强调了采用深度增强学习的声音优化技术的力量。

虽然近年来，深层加强学习代理人取得了前所未有的成功，但他们所学的政策可能是脆弱的，甚至无法概括到甚至略微修改他们的环境或不熟悉的情况。神经网络学习动态的黑匣子性质使得无法审核培训的深层代理并从这种失败中恢复过来。在本文中，我们提出了一种新颖的表示和学习方法来捕获环境动态而不使用神经网络。它起源于观察，在为人们设计的游戏中，动作的效果通常可以以连续的视觉观测的局部变化的形式感知。我们的算法旨在提取基于视觉的更改，并将其冷凝成一组依赖于依赖的描述性规则，我们调用“Visual Rewrite规则”（VRRS）。我们还提出了可以探索，扩展其规则集的VRR代理的初步结果，并通过规划与其学习的VRR世界模型来解决游戏。在若干古典游戏中，与几个主流深层代理相比，我们的非深度代理商证明了卓越的性能，极端样品效率和鲁棒泛化能力。

近年来，研究人员在设计了用于优化线性时间逻辑（LTL）目标和LTL的目标中的增强学习算法方面取得了重大进展。尽管有这些进步，但解决了这个问题的基本限制，以至于以前的研究暗示，但对我们的知识而言，尚未深入检查。在本文中，我们通过一般的LTL目标理解了学习的硬度。我们在马尔可夫决策过程（PAC-MDP）框架（PAC-MDP）框架中可能大致正确学习的问题正式化，这是一种测量加固学习中的样本复杂性的标准框架。在这一形式化中，我们证明，只有在LTL层次结构中最有限的类别中，才有于仅当公式中的最有限的类别，因此才能获得PAC-MDP的最佳政策。实际上，我们的结果意味着加强学习算法无法在与非有限范围可解除的LTL目标的无限环境的相互作用之后获得其学习政策的性能的PAC-MDP保证。

有限的线性时间逻辑（$ \ mathsf {ltl} _f $）是一种强大的正式表示，用于建模时间序列。我们解决了学习Compact $ \ Mathsf {ltl} _f $ formul的问题，从标记的系统行为的痕迹。我们提出了一部小说神经网络运营商，并评估结果架构，神经$ \ mathsf {ltl} _f $。我们的方法包括专用复发过滤器，旨在满足$ \ Mathsf {ltl} _f $ temporal运算符，以学习痕迹的高度准确的分类器。然后，它离散地激活并提取由学习权重表示的真相表。此实话表将转换为符号形式并作为学习公式返回。随机生成$ \ Mathsf {LTL} _F $公式显示神经$ \ MATHSF {LTL} _F $尺寸，比现有方法更大，即使在存在噪声时也保持高精度。

奖励是加强学习代理的动力。本文致力于了解奖励的表现，作为捕获我们希望代理人执行的任务的一种方式。我们在这项研究中涉及三个新的抽象概念“任务”，可能是可取的：（1）一组可接受的行为，（2）部分排序，或者（3）通过轨迹的部分排序。我们的主要结果证明，虽然奖励可以表达许多这些任务，但每个任务类型的实例都没有Markov奖励函数可以捕获。然后，我们提供一组多项式时间算法，其构造Markov奖励函数，允许代理优化这三种类型中的每种类型的任务，并正确确定何时不存在这种奖励功能。我们得出结论，具有证实和说明我们的理论发现的实证研究。

Dataset distillation has emerged as a prominent technique to improve data efficiency when training machine learning models. It encapsulates the knowledge from a large dataset into a smaller synthetic dataset. A model trained on this smaller distilled dataset can attain comparable performance to a model trained on the original training dataset. However, the existing dataset distillation techniques mainly aim at achieving the best trade-off between resource usage efficiency and model utility. The security risks stemming from them have not been explored. This study performs the first backdoor attack against the models trained on the data distilled by dataset distillation models in the image domain. Concretely, we inject triggers into the synthetic data during the distillation procedure rather than during the model training stage, where all previous attacks are performed. We propose two types of backdoor attacks, namely NAIVEATTACK and DOORPING. NAIVEATTACK simply adds triggers to the raw data at the initial distillation phase, while DOORPING iteratively updates the triggers during the entire distillation procedure. We conduct extensive evaluations on multiple datasets, architectures, and dataset distillation techniques. Empirical evaluation shows that NAIVEATTACK achieves decent attack success rate (ASR) scores in some cases, while DOORPING reaches higher ASR scores (close to 1.0) in all cases. Furthermore, we conduct a comprehensive ablation study to analyze the factors that may affect the attack performance. Finally, we evaluate multiple defense mechanisms against our backdoor attacks and show that our attacks can practically circumvent these defense mechanisms.

We present a dynamic path planning algorithm to navigate an amphibious rotor craft through a concave time-invariant obstacle field while attempting to minimize energy usage. We create a nonlinear quaternion state model that represents the rotor craft dynamics above and below the water. The 6 degree of freedom dynamics used within a layered architecture to generate motion paths for the vehicle to follow and the required control inputs. The rotor craft has a 3 dimensional map of its surroundings that is updated via limited range onboard sensor readings within the current medium (air or water). Path planning is done via PRM and D* Lite.