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Found 8 papers, 6 papers with code
DeepDrummer : Generating Drum Loops using Deep Learning and a Human in the Loop
DeepDrummer is a drum loop generation tool that uses active learning to learn the preferences (or current artistic intentions) of a human user from a small number of interactions.
BabyAI 1.1
This increases reinforcement learning sample efficiency by up to 3 times and improves imitation learning performance on the hardest level from 77 % to 90. 4 %.
Combating False Negatives in Adversarial Imitation Learning
In adversarial imitation learning, a discriminator is trained to differentiate agent episodes from expert demonstrations representing the desired behavior.
Options of Interest: Temporal Abstraction with Interest Functions
Temporal abstraction refers to the ability of an agent to use behaviours of controllers which act for a limited, variable amount of time.
Automated curriculum generation for Policy Gradients from Demonstrations
In this paper, we present a technique that improves the process of training an agent (using RL) for instruction following.
Option-Critic in Cooperative Multi-agent Systems
In this paper, we investigate learning temporal abstractions in cooperative multi-agent systems, using the options framework (Sutton et al, 1999).
Robo-PlaNet: Learning to Poke in a Day
Recently, the Deep Planning Network (PlaNet) approach was introduced as a model-based reinforcement learning method that learns environment dynamics directly from pixel observations.
BabyAI: A Platform to Study the Sample Efficiency of Grounded Language Learning
Allowing humans to interactively train artificial agents to understand language instructions is desirable for both practical and scientific reasons, but given the poor data efficiency of the current learning methods, this goal may require substantial research efforts.
