Nov 1, 2023 · In this work, we propose tailored self-IL sampling strategies by prioritizing transitions in different ways and extending prioritization techniques to PCG ...

Nov 1, 2023 · Exploration is a fundamental challenge in Reinforcement. Learning (RL), especially in scenarios with sparse rewards where the agent may struggle ...

Exploration poses a fundamental challenge in Reinforcement Learning (RL) with sparse rewards, limiting an agent's ability to learn optimal decision-making ...

We present an approach for procedural content generation (PCG), and improving generalization in reinforcement learning (RL) agents, by using adversarial ...

Dive into the research topics of 'Enhanced Generalization Through Prioritization and Diversity in Self-Imitation Reinforcement Learning Over Procedural ...

Sparse reward environments in reinforcement learning (RL) pose significant challenges for exploration, often leading to inefficient or incomplete learning ...

Enhanced Generalization through Prioritization and Diversity in Self-Imitation Reinforcement Learning over Procedural Environments with Sparse Rewards. A Andres ...

Enhanced Generalization Through Prioritization and Diversity in Self-Imitation Reinforcement Learning Over Procedural Environments with Sparse Rewards.

Sparse reward environments in reinforcement learning (RL) pose significant challenges for exploration, often leading to inefficient or incomplete learning ...

We also address diversity loss through modifications to counteract the impact of generalization requirements and bias introduced by prioritization techniques.