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Found 5 papers, 2 papers with code
Perturbation-based exploration methods in deep reinforcement learning
In this study we investigate the effect of perturbations in policy and reward spaces on the exploratory behavior of the agent.
Training spiking neural networks using reinforcement learning
Neurons in the brain communicate with each other through discrete action spikes as opposed to continuous signal transmission in artificial neural networks.
A memory enhanced LSTM for modeling complex temporal dependencies
In this paper, we present Gamma-LSTM, an enhanced long short term memory (LSTM) unit, to enable learning of hierarchical representations through multiple stages of temporal abstractions.
Reinforcement learning with a network of spiking agents
Neuroscientific theory suggests that dopaminergic neurons broadcast global reward prediction errors to large areas of the brain influencing the synaptic plasticity of the neurons in those regions.
Lexicase selection in Learning Classifier Systems
We show that batch-lexicase selection results in the creation of more generic rules which is favorable for generalization on future data.
