Ademi AdenijiEmail: ademi_adeniji [at] berkeley [dot] edu |
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I am a Computer Science PhD student at UC Berkeley advised by Pieter Abbeel. I'm interested in developing generalist agents capable of intelligent decision making in complex environments. I focus on leveraging reinforcement learning algorithms for enabling agents to autonomously collect diverse data and learn general-purpose behaviors with limited human supervision. I am supported by the Berkeley Chancellors Fellowship.
I previously interned at NVIDIA where I worked on reinforcement learning and robotics research as well as at Google where I worked on the SmartHome Automation team. I completed my BS and MS at Stanford University conducting research in the Stanford Vision and Learning Lab advised by Fei-Fei Li.
GitHub | LinkedIn | Twitter | Google Scholar | CV |
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Language Reward Modulation for Pretraining Reinforcement Learning
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Video Prediction Models as Rewards for Reinforcement Learning
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Skill-Based Reinforcement Learning with Intrinsic Reward Matching
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Learning Representations for Unsupervised Skill Discovery
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Recurrent Control Nets as Central Pattern Generators for Deep Reinforcement Learning
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Latent Actor-Critic with Intrisnic Motivation and Skill Hierarchy
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Latent Skill Transfer for Simulated Agents
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Volumetric Semantic Segmentation of Glioblastoma Tumors from MRI Studies
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Sequence-to-Sequence Generative Argumentative Dialogue Systems with Self-Attention
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