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Found 3 papers, 1 papers with code
Shortest-Path Constrained Reinforcement Learning for Sparse Reward Tasks
We propose the k-Shortest-Path (k-SP) constraint: a novel constraint on the agent's trajectory that improves the sample efficiency in sparse-reward MDPs.
Adaptive Learning Rule for Hardware-based Deep Neural Networks Using Electronic Synapse Devices
In this paper, we propose a learning rule based on a back-propagation (BP) algorithm that can be applied to a hardware-based deep neural network (HW-DNN) using electronic devices that exhibit discrete and limited conductance characteristics.
Micro-Objective Learning : Accelerating Deep Reinforcement Learning through the Discovery of Continuous Subgoals
To solve this issue, the subgoal and option framework have been proposed.
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