37 citations · 41 across the 5 of their papers we have counts for
4 papers · 1 filter
Perturbation-based exploration methods in deep reinforcement learning
Sneha Aenugu
Recent research on structured exploration placed emphasis on identifying novel states in the state space and incentivizing the agent to revisit them through intrinsic reward bonuse…
Training spiking neural networks using reinforcement learning
Sneha Aenugu
Neurons in the brain communicate with each other through discrete action spikes as opposed to continuous signal transmission in artificial neural networks. Therefore, the tradition…
Reinforcement learning with a network of spiking agents
Sneha Aenugu, Abhishek Sharma, Sasikiran Yelamarthi +3
Neuroscientific theory suggests that dopaminergic neurons broadcast global reward prediction errors to large areas of the brain influencing the synaptic plasticity of the neurons i…
A memory enhanced LSTM for modeling complex temporal dependencies
Sneha Aenugu
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 abstrac…