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Found 6 papers, 1 papers with code
Policy Distillation with Selective Input Gradient Regularization for Efficient Interpretability
Although deep Reinforcement Learning (RL) has proven successful in a wide range of tasks, one challenge it faces is interpretability when applied to real-world problems.
Neuroevolution of a Recurrent Neural Network for Spatial and Working Memory in a Simulated Robotic Environment
Animals ranging from rats to humans can demonstrate cognitive map capabilities.
Domain Adaptation In Reinforcement Learning Via Latent Unified State Representation
To address this issue, we propose a two-stage RL agent that first learns a latent unified state representation (LUSR) which is consistent across multiple domains in the first stage, and then do RL training in one source domain based on LUSR in the second stage.
Neuromodulated Patience for Robot and Self-Driving Vehicle Navigation
In the present paper, we take inspiration from the serotonergic system and apply it to the task of robot navigation.
Attention-Based Structural-Plasticity
Catastrophic forgetting/interference is a critical problem for lifelong learning machines, which impedes the agents from maintaining their previously learned knowledge while learning new tasks.
Neuromodulated Goal-Driven Perception in Uncertain Domains
In uncertain domains, the goals are often unknown and need to be predicted by the organism or system.
