3 papers
cs.LG2025
Meta-Learning to Explore via Memory Density Feedback
Kevin McKee, Eric Alt, Andrew Grebenisan +2
Exploration algorithms for reinforcement learning typically replace or augment the reward function with an additional ``intrinsic'' reward that trains the agent to seek previously…
cs.LG2025
A Method of Selective Attention for Reservoir Based Agents
Kevin McKee
Training of deep reinforcement learning agents is slowed considerably by the presence of input dimensions that do not usefully condition the reward function. Existing modules such…
cs.LG2024
Reservoir Computing for Fast, Simplified Reinforcement Learning on Memory Tasks
Kevin McKee
Tasks in which rewards depend upon past information not available in the current observation set can only be solved by agents that are equipped with short-term memory. Usual choice…