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1. Is Long Horizon RL More Difficult Than Short Horizon RL?

   Wang, Ruosong ; Du, Simon S. ; Yang, Lin ; Kakade, Sham ( January 2020 , Advances in neural information processing systems)

   null (Ed.)

   Learning to plan for long horizons is a central challenge in episodic reinforcement learning problems. A fundamental question is to understand how the difficulty of the problem scales as the horizon increases. Here the natural measure of sample complexity is a normalized one: we are interested in the \emph{number of episodes} it takes to provably discover a policy whose value is eps near to that of the optimal value, where the value is measured by the \emph{normalized} cumulative reward in each episode. In a COLT 2018 open problem, Jiang and Agarwal conjectured that, for tabular, episodic reinforcement learning problems, there exists a sample complexity lower bound which exhibits a polynomial dependence on the horizon --- a conjecture which is consistent with all known sample complexity upper bounds. This work refutes this conjecture, proving that tabular, episodic reinforcement learning is possible with a sample complexity that scales only \emph{logarithmically} with the planning horizon. In other words, when the values are appropriately normalized (to lie in the unit interval), this results shows that long horizon RL is no more difficult than short horizon RL, at least in a minimax sense. Our analysis introduces two ideas: (i) the construction of an eps-net for near-optimal policies whose log-covering number scales only logarithmically with the planning horizon, and (ii) the Online Trajectory Synthesis algorithm, which adaptively evaluates all policies in a given policy class and enjoys a sample complexity that scales logarithmically with the cardinality of the given policy class. Both may be of independent interest. 

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2. Reward-Free RL is No Harder Than Reward-Aware RL in Linear Markov Decision Processes

   Wagenmaker, Andrew ; Chen, Yifang ; Simchowitz, Max ; Du, Simon S ; Jamieson, Kevin ( January 2022 , International Conference on Machine Learning)
3. Reward-Free RL is No Harder Than Reward-Aware RL in Linear Markov Decision Processes

   Wagenmaker, Andrew J ; Chen, Yifang ; Simchowitz, Max ; Du, Simon ; Jamieson, Kevin ( January 2022 , Proceedings of Machine Learning Research)
4. Targeted Attack on Deep RL-based Autonomous Driving with Learned Visual Patterns

   https://doi.org/10.1109/ICRA46639.2022.9811574  

   Buddareddygari, Prasanth ; Zhang, Travis ; Yang, Yezhou ; Ren, Yi ( May 2022 , 2022 International Conference on Robotics and Automation (ICRA))

   Recent studies demonstrated the vulnerability of control policies learned through deep reinforcement learning against adversarial attacks, raising concerns about the application of such models to risk-sensitive tasks such as autonomous driving. Threat models for these demonstrations are limited to (1) targeted attacks through real-time manipulation of the agent's observation, and (2) untargeted attacks through manipulation of the physical environment. The former assumes full access to the agent's states/observations at all times, while the latter has no control over attack outcomes. This paper investigates the feasibility of targeted attacks through visually learned patterns placed on physical objects in the environment, a threat model that combines the practicality and effectiveness of the existing ones. Through analysis, we demonstrate that a pre-trained policy can be hijacked within a time window, e.g., performing an unintended self-parking, when an adversarial object is present. To enable the attack, we adopt an assumption that the dynamics of both the environment and the agent can be learned by the attacker. Lastly, we empirically show the effectiveness of the proposed attack on different driving scenarios, perform a location robustness test, and study the tradeoff between the attack strength and its effectiveness Code is available at https://github.com/ASU-APG/ Targeted-Physical-Adversarial-Attacks-on-AD 

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5. An interpretable RL framework for pre-deployment modeling in ICU hypotension management

   https://doi.org/10.1038/s41746-022-00708-4  

   Zhang, Kristine ; Wang, Henry ; Du, Jianzhun ; Chu, Brian ; Arévalo, Aldo Robles ; Kindle, Ryan ; Celi, Leo Anthony ; Doshi-Velez, Finale ( December 2022 , npj Digital Medicine)

   Abstract Computational methods from reinforcement learning have shown promise in inferring treatment strategies for hypotension management and other clinical decision-making challenges. Unfortunately, the resulting models are often difficult for clinicians to interpret, making clinical inspection and validation of these computationally derived strategies challenging in advance of deployment. In this work, we develop a general framework for identifying succinct sets of clinical contexts in which clinicians make very different treatment choices, tracing the effects of those choices, and inferring a set of recommendations for those specific contexts. By focusing on these few key decision points, our framework produces succinct, interpretable treatment strategies that can each be easily visualized and verified by clinical experts. This interrogation process allows clinicians to leverage the model’s use of historical data in tandem with their own expertise to determine which recommendations are worth investigating further e.g. at the bedside. We demonstrate the value of this approach via application to hypotension management in the ICU, an area with critical implications for patient outcomes that lacks data-driven individualized treatment strategies; that said, our framework has broad implications on how to use computational methods to assist with decision-making challenges on a wide range of clinical domains. 

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