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Found 6 papers, 1 papers with code
Explaining Agent's Decision-making in a Hierarchical Reinforcement Learning Scenario
The results obtained showed that it is possible to use the memory-based method in hierarchical environments with high-level tasks and compute the probabilities of success to be used as a basis for explaining the agent's behavior.
Reinforcement Learning for UAV control with Policy and Reward Shaping
RL is a machine learning paradigm wherein an agent interacts with an environment to solve a given task.
Explainable Deep Reinforcement Learning Using Introspection in a Non-episodic Task
Explainable reinforcement learning allows artificial agents to explain their behavior in a human-like manner aiming at non-expert end-users.
Learning Proxemic Behavior Using Reinforcement Learning with Cognitive Agents
This behavior is an essential part of the communication process due to delimit the acceptable distance to interact with another being.
KutralNet: A Portable Deep Learning Model for Fire Recognition
The experiments show that our model keeps high accuracy while substantially reducing the number of parameters and flops.
Deep Reinforcement Learning with Interactive Feedback in a Human-Robot Environment
We compare three different learning methods using a simulated robotic arm for the task of organizing different objects; the proposed methods are (i) deep reinforcement learning (DeepRL); (ii) interactive deep reinforcement learning using a previously trained artificial agent as an advisor (agent-IDeepRL); and (iii) interactive deep reinforcement learning using a human advisor (human-IDeepRL).
