Srinivasan Sriram

Title: The Innovative Contributions of Srinivasan Sriram

Introduction

Srinivasan Sriram is a notable inventor based in London, GB, recognized for his significant contributions to the field of reinforcement learning. His work primarily revolves around developing methods, systems, and apparatus that greatly enhance the capabilities of neural networks, particularly in their interactions with dynamic environments.

Latest Patents

Sriram holds a patent titled "Reinforcement Learning Using Pseudo-Counts." This innovative patent outlines techniques for training a neural network that selects actions to be executed by an agent engaged with an environment. The process includes obtaining data on the first observation that characterizes the environmental state, the action taken by the agent in response, and the actual reward received from that action. Additionally, it involves determining a pseudo-count for the observation, generating an exploration reward bonus to motivate exploration, and adjusting the neural network's parameters with the combined reward of the actual and exploration rewards.

Career Highlights

Srinivasan Sriram is a dedicated employee at DeepMind Technologies Limited, where he has fostered a reputation for innovation and expertise in artificial intelligence. His commitment to pushing the boundaries in machine learning is evident in the quality and applicability of his research.

Collaborations

During his career, Sriram has collaborated with esteemed colleagues such as Marc Gendron-Bellemare and Remi Munos. These partnerships have enriched his work, allowing for a robust exchange of ideas that drive advancements in AI and reinforcement learning.

Conclusion

Srinivasan Sriram's contributions to innovation, particularly in reinforcement learning, underscore his role as a significant inventor in the technology sector. His patented work not only advances theoretical understanding but also enhances practical applications, influencing the future of artificial intelligence within various environments.