1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.LG2021★ 1 cited
Subgoal-based Reward Shaping to Improve Efficiency in Reinforcement Learning
Takato Okudo, Seiji Yamada
Reinforcement learning, which acquires a policy maximizing long-term rewards, has been actively studied. Unfortunately, this learning type is too slow and difficult to use in pract…
cs.RO2021
Reward Shaping with Subgoals for Social Navigation
Takato Okudo, Seiji Yamada
Social navigation has been gaining attentions with the growth in machine intelligence. Since reinforcement learning can select an action in the prediction phase at a low computatio…
cs.LG2021
Reward Shaping with Dynamic Trajectory Aggregation
Takato Okudo, Seiji Yamada
Reinforcement learning, which acquires a policy maximizing long-term rewards, has been actively studied. Unfortunately, this learning type is too slow and difficult to use in pract…