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Advancing Deep Reinforcement Learning with NetHack, w/ Tim Rocktäschel - #527

2021-10-14 · 43 min · episode 527 · 16 entities

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    Advancing Deep Reinforcement Learning with NetHack, w/ Tim Rocktäschel - #527
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Episode description as stored
Take our survey at twimlai.com/survey21! Today we’re joined by Tim Rocktäschel, a research scientist at Facebook AI Research and an associate professor at University College London (UCL).  Tim’s work focuses on training RL agents in simulated environments, with the goal of these agents being able to generalize to novel situations. Typically, this is done in environments like OpenAI Gym, MuJuCo, or even using Atari games, but these all come with constraints. In Tim’s approach, he utilizes a game called NetHack, which is much more rich and complex than the aforementioned environments.   In our conversation with Tim, we explore the ins and outs of using NetHack as a training environment, including how much control a user has when generating each individual game and the challenges he's faced when deploying the agents. We also discuss his work on MiniHack, an environment creation framework and suite of tasks that are based on NetHack, and future directions for this research. The complete show notes for this episode can be found at twimlai.com/go/527.