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Found 4 papers, 3 papers with code
Scilab-RL: A software framework for efficient reinforcement learning and cognitive modeling research
One problem with researching cognitive modeling and reinforcement learning (RL) is that researchers spend too much time on setting up an appropriate computational framework for their experiments.
Language-Conditioned Reinforcement Learning to Solve Misunderstandings with Action Corrections
To evaluate our approach, we propose a collection of benchmark environments for action correction in language-conditioned reinforcement learning, utilizing a synthetic instructor to generate language goals and their corresponding corrections.
Grounding Hindsight Instructions in Multi-Goal Reinforcement Learning for Robotics
We show that hindsight instructions improve the learning performance, as expected.
Curious Hierarchical Actor-Critic Reinforcement Learning
Hierarchical abstraction and curiosity-driven exploration are two common paradigms in current reinforcement learning approaches to break down difficult problems into a sequence of simpler ones and to overcome reward sparsity.
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