Understanding Reinforcement Learning

We’re joined by Kamyar Azizzadenesheli, a staff researcher at Nvidia. During the talk, Kamyar provides us with an update on the most recent advancements in reinforcement learning (RL) and how the RL community is utilizing large language models’ (LLMs’) capacity for abstract reasoning. In a number of examples, including ALOHA, a robot that can learn to fold clothing, and Voyager, an RL agent that leverages GPT-4 to surpass earlier systems at playing Minecraft, Kamyar presents his ideas on how LLMs are advancing RL performance. We also look at the developments in evaluating and mitigating the risks associated with RL-based decision-making in industries including banking, medical, and agriculture. Lastly, we talk about Kamyar’s top predictions for the field of deep reinforcement learning, its future, and how achieving general intelligence will depend heavily on increased processing power.

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