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1. Cheaper and Faster: Distributed Deep Reinforcement Learning with Serverless Computing

   https://doi.org/10.1609/aaai.v38i15.29592  

   Yu, Hanfei; Li, Jian; Hua, Yang; Yuan, Xu; Wang, Hao (March 2024, Proceedings of the AAAI Conference on Artificial Intelligence)

   Deep reinforcement learning (DRL) has gained immense success in many applications, including gaming AI, robotics, and system scheduling. Distributed algorithms and architectures have been vastly proposed (e.g., actor-learner architecture) to accelerate DRL training with large-scale server-based clusters. However, training on-policy algorithms with the actor-learner architecture unavoidably induces resource wasting due to synchronization between learners and actors, thus resulting in significantly extra billing. As a promising alternative, serverless computing naturally fits on-policy synchronization and alleviates resource wasting in distributed DRL training with pay-as-you-go pricing. Yet, none has leveraged serverless computing to facilitate DRL training. This paper proposes MinionsRL, the first serverless distributed DRL training framework that aims to accelerate DRL training- and cost-efficiency with dynamic actor scaling. We prototype MinionsRL on top of Microsoft Azure Container Instances and evaluate it with popular DRL tasks from OpenAI Gym. Extensive experiments show that MinionsRL reduces total training time by up to 52% and training cost by 86% compared to latest solutions. 

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2. Physics-Regulated Deep Reinforcement Learning: Invariant Embeddings

   Cao, Hongpeng; Mao, Yanbing; Sha, Lui; Caccamo, Marco (May 2024, The Twelfth International Conference on Learning Representations)

   This paper proposes the Phy-DRL: a physics-regulated deep reinforcement learning (DRL) framework for safety-critical autonomous systems. The Phy-DRL has three distinguished invariant-embedding designs: i) residual action policy (i.e., integrating data-driven-DRL action policy and physics-model-based action policy), ii) automatically constructed safety-embedded reward, and iii) physics-model-guided neural network (NN) editing, including link editing and activation editing. Theoretically, the Phy-DRL exhibits 1) a mathematically provable safety guarantee and 2) strict compliance of critic and actor networks with physics knowledge about the action-value function and action policy. Finally, we evaluate the Phy-DRL on a cart-pole system and a quadruped robot. The experiments validate our theoretical results and demonstrate that Phy-DRL features guarantee safety compared to purely data-driven DRL and solely model-based design, while offering remarkably fewer learning parameters and fast training towards safety guarantee. 

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3. Leveraging Deep Reinforcement Learning for Pedagogical Policy Induction in an Intelligent Tutoring System

   Ausin, M. S. (January 2019, In: Proceedings of the 12th International Conference on Educational Data Mining (EDM 2019),)

   Deep Reinforcement Learning (DRL) has been shown to be a very powerful technique in recent years on a wide range of applications. Much of the prior DRL work took the online learning approach. However, given the challenges of building accurate simulations for modeling student learning, we investigated applying DRL to induce a pedagogical policy through an offiine approach. In this work, we explored the effectiveness of offiine DRL for pedagogical policy induction in an Intelligent Tutoring System. Generally speaking, when applying offiine DRL, we face two major challenges: one is limited training data and the other is the credit assignment problem caused by delayed rewards. In this work, we used Gaussian Processes to solve the credit assignment problem by estimating the inferred immediate rewards from the final delayed rewards. We then applied the DQN and Double-DQN algorithms to induce adaptive pedagogical strategies tailored to individual students. Our empirical results show that without solving the credit assignment problem, the DQN policy, although better than Double-DQN, was no better than a random policy. However, when combining DQN with the inferred rewards, our best DQN policy can outperform the random yet reasonable policy, especially for students with high pre-test scores. 

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4. On-Chip Optimization and Deep Reinforcement Learning in Memristor Based Computing

   https://doi.org/10.1145/3611315.3633242  

   Alam, Shahanur; Yakopcic, Chris; Taha, Tarek M (December 2023, IEEE)

   Reinforcement learning (RL) has shown its viability to learn when an agent interacts continually with the environment to optimize a policy. This work presents a memristor-based deep reinforcement learning (Mem-DRL) system for on-chip training, where the learning process takes place in a dynamic cartpole environment. Memristor device variability is taken into account to make the study more realistic. The proposed system utilized an analog ReLu module to reduce analog to digital converter usage. The analog Mem-DRL system consumed 191 times less energy than an optimized digital FP16 computing system. Our Mem-DRL system reduced the ADC usages by 40%, which led to reduced the overall system energy by 42%. Mem-DRL is 2.4 times faster than the FP16 system and performs 9.27 GOPS during DRL training. The system exhibited an energy efficiency of 23.8 TOPS/W. 

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5. Asynchronous parallel reinforcement learning for optimizing propulsive performance in fin ray control

   https://doi.org/10.1007/s00366-024-02093-w  

   Liu, Xin-Yang; Bodaghi, Dariush; Xue, Qian; Zheng, Xudong; Wang, Jian-Xun (December 2024, Engineering with Computers)

   Abstract Fish fin rays constitute a sophisticated control system for ray-finned fish, facilitating versatile locomotion within complex fluid environments. Despite extensive research on the kinematics and hydrodynamics of fish locomotion, the intricate control strategies in fin-ray actuation remain largely unexplored. While deep reinforcement learning (DRL) has demonstrated potential in managing complex nonlinear dynamics; its trial-and-error nature limits its application to problems involving computationally demanding environmental interactions. This study introduces a cutting-edge off-policy DRL algorithm, interacting with a fluid–structure interaction (FSI) environment to acquire intricate fin-ray control strategies tailored for various propulsive performance objectives. To enhance training efficiency and enable scalable parallelism, an innovative asynchronous parallel training (APT) strategy is proposed, which fully decouples FSI environment interactions and policy/value network optimization. The results demonstrated the success of the proposed method in discovering optimal complex policies for fin-ray actuation control, resulting in a superior propulsive performance compared to the optimal sinusoidal actuation function identified through a parametric grid search. The merit and effectiveness of the APT approach are also showcased through comprehensive comparison with conventional DRL training strategies in numerical experiments of controlling nonlinear dynamics. 

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