5 citations · 14 across the 10 of their papers we have counts for
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cs.RO2021★ 2 cited
Marginal MAP Estimation for Inverse RL under Occlusion with Observer Noise
Prasanth Sengadu Suresh, Prashant Doshi
We consider the problem of learning the behavioral preferences of an expert engaged in a task from noisy and partially-observable demonstrations. This is motivated by real-world ap…
cs.LG2021★ 1 cited
Many Agent Reinforcement Learning Under Partial Observability
Keyang He, Prashant Doshi, Bikramjit Banerjee
Recent renewed interest in multi-agent reinforcement learning (MARL) has generated an impressive array of techniques that leverage deep reinforcement learning, primarily actor-crit…