Agents trained by reinforcement learning (RL) often fail to generalize beyond the environment they were trained in, even when presented with new scenarios that seem similar to the training environment. We study the query complexity required to train RL agents that generalize to multiple environments. Intuitively, tractable generalization is only possible when the environments are similar or close in some sense. To capture this, we introduce Weak Proximity, a natural structural condition that requires the environments to have highly similar transition and reward functions and share a policy providing optimal value. Despite such shared structure, we prove that tractable generalization is impossible in the worst case. This holds even when each individual environment can be efficiently solved to obtain an optimal linear policy, and when the agent possesses a generative model. Our lower bound applies to the more complex task of representation learning for the purpose of efficient generalization to multiple environments. On the positive side, we introduce Strong Proximity, a strengthened condition which we prove is sufficient for efficient generalization.
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InitLight: Initial Model Generation for Traffic Signal Control Using Adversarial Inverse Reinforcement Learning
Due to repetitive trial-and-error style interactions between agents and a fixed traffic environment during the policy learning, existing Reinforcement Learning (RL)-based Traffic Signal Control (TSC) methods greatly suffer from long RL training time and poor adaptability of RL agents to other complex traffic environments. To address these problems, we propose a novel Adversarial Inverse Reinforcement Learning (AIRL)-based pre-training method named InitLight, which enables effective initial model generation for TSC agents. Unlike traditional RL-based TSC approaches that train a large number of agents simultaneously for a specific multi-intersection environment, InitLight pretrains only one single initial model based on multiple single-intersection environments together with
their expert trajectories. Since the reward function learned by InitLight can recover ground-truth TSC rewards for different intersections at optimality, the pre-trained agent can be deployed at intersections of any traffic environments as initial models to accelerate subsequent overall global RL training. Comprehensive experimental results show that, the
initial model generated by InitLight can not only significantly accelerate the convergence with much fewer episodes, but also own superior generalization ability to accommodate various kinds of complex traffic environments.
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- Award ID(s):
- 2217104
- NSF-PAR ID:
- 10464779
- Date Published:
- Journal Name:
- IJCAI
- ISSN:
- 1045-0823
- Page Range / eLocation ID:
- 4949-4958
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Pedagogical planners can provide adaptive support to students in narrative-centered learning environments by dynamically scaffolding student learning and tailoring problem scenarios. Reinforcement learning (RL) is frequently used for pedagogical planning in narrative-centered learning environments. However, RL-based pedagogical planning raises significant challenges due to the scarcity of data for training RL policies. Most prior work has relied on limited-size datasets and offline RL techniques for policy learning. Unfortunately, offline RL techniques do not support on-demand exploration and evaluation, which can adversely impact the quality of induced policies. To address the limitation of data scarcity and offline RL, we propose INSIGHT, an online RL framework for training data-driven pedagogical policies that optimize student learning in narrative-centered learning environments. The INSIGHT framework consists of three components: a narrative-centered learning environment simulator, a simulated student agent, and an RL-based pedagogical planner agent, which uses a reward metric that is associated with effective student learning processes. The framework enables the generation of synthetic data for on-demand exploration and evaluation of RL-based pedagogical planning. We have implemented INSIGHT with OpenAI Gym for a narrative-centered learning environment testbed with rule-based simulated student agents and a deep Q-learning-based pedagogical planner. Our results show that online deep RL algorithms can induce near-optimal pedagogical policies in the INSIGHT framework, while offline deep RL algorithms only find suboptimal policies even with large amounts of data.
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Connected vehicle (CV) technologies enable data exchange between vehicles and transportation infrastructure. In a CV environment, traffic signal control systems receive CV trajectory data through vehicle-to-infrastructure (V2I) communications to make control decisions. Comparing with existing data collection methods (e.g., from loop-detectors), the CV trajectory data provide much richer information, and therefore have great potentials to improve the system performance by reducing total vehicle delay at signalized intersections. However, this connectivity might also bring cyber security concerns. In this paper, we aim to investigate the security problem of CV-based traffic signal control (CV-TSC) systems. Specifically, we focus on evaluating the impact of falsified data attacks on the system performance. A black-box attack scenario, in which the control logic of a CV-TSC system is unavailable to attackers, is considered. A two-step attack model is constructed. In the first step, the attacker tries to learn the control logic using a surrogate model. Based on the surrogate model, in the second step, the attacker launches falsified data attacks to influence the control systems to make sub-optimal control decisions. In the case study, we apply the attack model to an existing CV-TSC system (i.e., I-SIG) and find intersection delay can be significantly increased. Finally, we discuss some promising defense directions.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
