Aryaman Reddi 🚀
Aryaman Reddi ආර්යමන් රෙඩ්ඩි

PhD Student

Technical University of Darmstadt

About Me

I am a PhD student in reinforcement learning at the LiteRL group at the Technical University of Darmstadt in partnership with the Intelligent Autonomous Systems lab and Hessian.AI, supervised by Professor Carlo D’Eramo 🎓

I am interested in developing sample-efficient techniques in deep multi-agent reinforcement learning using insights from game theory 🕹️

I am a continuum hypothesis skeptic, a mereological universalist, and a Collatz conjecture supporter.

CV
Interests
  • Reinforcement Learning
  • Game Theory
  • Ethics and Philosophy
Education

  • PhD Computer Science

    Technical University of Darmstadt, Germany

  • MEng & BA Information and Computer Engineering

    University of Cambridge, United Kingdom

Experience

  1. Machine Learning Research Engineer

    Arm, Cambridge, United Kingdom
    • Developed an open-source tool (ML Inference Advisor) to optimise neural networks for inference on Arm NPUs using Python (PyTorch, Numpy, Jupyter, Pandas), C++, Kubernetes & Docker.
    • Improved processing efficiency for floating point operations in a class-A Arm NPU bridge by 14%
    • Used machine learning clustering, kernel regression, and principal component analysis to improve verification coverage in an Arm CPU bridge by 11%

Education

  1. PhD Computer Science

    Technical University of Darmstadt, Germany

    My focus is on developing sample-efficient algorithms for exploration, coordination, and communication in multi-agent reinforcement learning using insights from game theory.

    I believe bridging the gap between practical deep learning and theoretical models of stochastic optimisation is essential for scaling RL in real-world MARL settings.

    I build algorithms which exhibit high performance in high-dimensional environments while providing mathematical insights using probability theory, linear algebra, calculus, & functional analysis.

  2. MEng & BA Information and Computer Engineering

    University of Cambridge, United Kingdom
    • Grade: Distinction (GPA 4.0 Equivalent)
    • Received the David Thompson prize for academic achievement
    Read Thesis
Papers
Deep Learning Agents Trained For Avoidance Behave Like Hawks And Doves
Dynamic Obstacle Avoidance with Bounded Rationality Adversarial Reinforcement Learning
Robot Learning

Dynamic Obstacle Avoidance with Bounded Rationality Adversarial Reinforcement Learning

Reinforcement Learning (RL) has proven largely effective in obtaining stable locomotion gaits for legged robots. However, designing control algorithms which can robustly navigate unseen environments with obstacles remains an ongoing problem within quadruped locomotion. To tackle this, it is convenient to solve navigation tasks by means of a hierarchical approach with a low-level locomotion policy and a high-level navigation policy. Crucially, the high-level policy needs to be robust to dynamic obstacles along the path of the agent. In this work, we propose a novel way to endow navigation policies with robustness by a training process that models obstacles as adversarial agents, following the adversarial RL paradigm. Importantly, to improve the reliability of the training process, we bound the rationality of the adversarial agent resorting to quantal response equilibria, and place a curriculum over its rationality. We called this method Hierarchical policies via Quantal response Adversarial Reinforcement Learning (Hi-QARL). We demonstrate the robustness of our method by benchmarking it in unseen randomized mazes with multiple obstacles. To prove its applicability in real scenarios, our method is applied on a Unitree GO1 robot in simulation.

Robust Adversarial Reinforcement Learning via Bounded Rationality Curricula
Adversarial Reinforcement Learning

Robust Adversarial Reinforcement Learning via Bounded Rationality Curricula

Robustness against adversarial attacks and distribution shifts is a long-standing goal of Reinforcement Learning (RL). To this end, Robust Adversarial Reinforcement Learning (RARL) trains a protagonist against destabilizing forces exercised by an adversary in a competitive zero-sum Markov game, whose optimal solution, i.e., rational strategy, corresponds to a Nash equilibrium. However, finding Nash equilibria requires facing complex saddle point optimization problems, which can be prohibitive to solve, especially for high-dimensional control. In this paper, we propose a novel approach for adversarial RL based on entropy regularization to ease the complexity of the saddle point optimization problem. We show that the solution of this entropy-regularized problem corresponds to a Quantal Response Equilibrium (QRE), a generalization of Nash equilibria that accounts for bounded rationality, i.e., agents sometimes play random actions instead of optimal ones. Crucially, the connection between the entropy-regularized objective and QRE enables free modulation of the rationality of the agents by simply tuning the temperature coefficient. We leverage this insight to propose our novel algorithm, Quantal Adversarial RL (QARL), which gradually increases the rationality of the adversary in a curriculum fashion until it is fully rational, easing the complexity of the optimization problem while retaining robustness. We provide extensive evidence of QARL outperforming RARL and recent baselines across several MuJoCo locomotion and navigation problems in overall performance and robustness.

Talks
Interview with Hessian.AI: How adversarial reinforcement learning trains robust AI 🟣
Blog
International Conference on Learning Representations (ICLR) 2024 📜
Symposium on Lifelong Explainable Robot Learning (SYMPLER) 2023 🦾
A Tier List of the Jurors from 12 Angry Men (1957) ⚖️
How to use Text-to-Speech in WSL to inform you when a job has finished 💻
Projects
Animating the Collatz Conjecture 💫
Machine Unlearning 🤖
More

Communities

Interesting links

Contact

✉️ aryaman{}reddi{}tu-darmstadt.de

📍 E327, S2|02 Robert-Piloty-Gebäude, Technical University of Darmstadt, Darmstadt 64289

LinkedIn