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Found 6 papers, 2 papers with code
An Online Data-Driven Emergency-Response Method for Autonomous Agents in Unforeseen Situations
We demonstrate the potential of this approach in a simulated 3D car driving scenario, in which the agent devises a response in under 2 seconds to avoid collisions with objects it has not seen during training.
Evolving Inborn Knowledge For Fast Adaptation in Dynamic POMDP Problems
Rapid online adaptation to changing tasks is an important problem in machine learning and, recently, a focus of meta-reinforcement learning.
Deep Reinforcement Learning with Modulated Hebbian plus Q Network Architecture
This paper presents a new neural architecture that combines a modulated Hebbian network (MOHN) with DQN, which we call modulated Hebbian plus Q network architecture (MOHQA).
Generative Continual Concept Learning
After learning a concept, humans are also able to continually generalize their learned concepts to new domains by observing only a few labeled instances without any interference with the past learned knowledge.
Continual Learning Using World Models for Pseudo-Rehearsal
Here we propose a method to continually learn these internal world models through the interleaving of internally generated episodes of past experiences (i. e., pseudo-rehearsal).
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.
