50 citations · 163 across the 21 of their papers we have counts for
3 papers · 1 filter
Lights out: training RL agents robust to temporary blindness
N. Ordonez, M. Tromp, P. M. Julbe +1
Agents trained with DQN rely on an observation at each timestep to decide what action to take next. However, in real world applications observations can change or be missing entire…
Exploration with Unreliable Intrinsic Reward in Multi-Agent Reinforcement Learning
Wendelin Böhmer, Tabish Rashid, Shimon Whiteson
This paper investigates the use of intrinsic reward to guide exploration in multi-agent reinforcement learning. We discuss the challenges in applying intrinsic reward to multiple c…
Non-Deterministic Policy Improvement Stabilizes Approximated Reinforcement Learning
Wendelin Böhmer, Rong Guo, Klaus Obermayer
This paper investigates a type of instability that is linked to the greedy policy improvement in approximated reinforcement learning. We show empirically that non-deterministic pol…