39 citations · 198 across the 21 of their papers we have counts for
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cs.LG2023★ 1 cited
A State Representation for Diminishing Rewards
Ted Moskovitz, Samo Hromadka, Ahmed Touati +2
A common setting in multitask reinforcement learning (RL) demands that an agent rapidly adapt to various stationary reward functions randomly sampled from a fixed distribution. In…
cs.LG2023★ 1 cited
Representations and Exploration for Deep Reinforcement Learning using Singular Value Decomposition
Yash Chandak, Shantanu Thakoor, Zhaohan Daniel Guo +4
Representation learning and exploration are among the key challenges for any deep reinforcement learning agent. In this work, we provide a singular value decomposition based method…