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Found 8 papers, 2 papers with code
Unsupervised state representation learning with robotic priors: a robustness benchmark
We reproduce this simplification process using a neural network to build a low dimensional state representation of the world from images acquired by a robot.
Visual Reasoning with Multi-hop Feature Modulation
Recent breakthroughs in computer vision and natural language processing have spurred interest in challenging multi-modal tasks such as visual question-answering and visual dialogue.
Self-Educated Language Agent with Hindsight Experience Replay for Instruction Following
Language creates a compact representation of the world and allows the description of unlimited situations and objectives through compositionality.
I'm sorry Dave, I'm afraid I can't do that, Deep Q-learning from forbidden action
Violating constraints thus results in rejected actions or entering in a safe mode driven by an external controller, making RL agents incapable of learning from their mistakes.
HIGhER : Improving instruction following with Hindsight Generation for Experience Replay
Language creates a compact representation of the world and allows the description of unlimited situations and objectives through compositionality.
A Machine of Few Words -- Interactive Speaker Recognition with Reinforcement Learning
To do so, we cast the speaker recognition task into a sequential decision-making problem that we solve with Reinforcement Learning.
Relevant Action Matters : Motivating agent with action usefulness
We evaluate RAM on the procedurally-generated environment MiniGrid, against state-of-the-art methods.
Don't Do What Doesn't Matter: Intrinsic Motivation with Action Usefulness
Sparse rewards are double-edged training signals in reinforcement learning: easy to design but hard to optimize.
