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Found 6 papers, 2 papers with code
Preserving Silent Features for Domain Generalization
In light of this, we propose a simple yet effective method termed STEP (Silent Feature Preservation) to improve the generalization performance of the self-supervised contrastive learning pre-trained model by alleviating the suppression of silent features during the supervised fine-tuning process.
Learning Invariable Semantical Representation from Language for Extensible Policy Generalization
Recently, incorporating natural language instructions into reinforcement learning (RL) to learn semantically meaningful representations and foster generalization has caught many concerns.
Adjacency constraint for efficient hierarchical reinforcement learning
Searching in a large goal space poses difficulty for both high-level subgoal generation and low-level policy learning.
Subjective Learning for Open-Ended Data
In this paper, we present a novel supervised learning framework of learning from open-ended data, which is modeled as data implicitly sampled from multiple domains with the data in each domain obeying a domain-specific target function.
CRIL: Continual Robot Imitation Learning via Generative and Prediction Model
Imitation learning (IL) algorithms have shown promising results for robots to learn skills from expert demonstrations.
Generating Adjacency-Constrained Subgoals in Hierarchical Reinforcement Learning
In this paper, we show that this problem can be effectively alleviated by restricting the high-level action space from the whole goal space to a $k$-step adjacent region of the current state using an adjacency constraint.
