Curiosity-driven exploration in sparse-reward multi-agent reinforcement learning
J Li, P Gajane - arxiv preprint arxiv:2302.10825, 2023 - arxiv.org
Sparsity of rewards while applying a deep reinforcement learning method negatively affects
its sample-efficiency. A viable solution to deal with the sparsity of rewards is to learn via …
its sample-efficiency. A viable solution to deal with the sparsity of rewards is to learn via …
Subgoal-based Hierarchical Reinforcement Learning for Multi-Agent Collaboration
Recent advancements in reinforcement learning have made significant impacts across
various domains, yet they often struggle in complex multi-agent environments due to issues …
various domains, yet they often struggle in complex multi-agent environments due to issues …
Towards Improving Exploration in Self-Imitation Learning using Intrinsic Motivation
Reinforcement Learning has emerged as a strong alternative to solve optimization tasks
efficiently. The use of these algorithms highly depends on the feedback signals provided by …
efficiently. The use of these algorithms highly depends on the feedback signals provided by …
Words as Beacons: Guiding RL Agents with High-Level Language Prompts
U Ruiz-Gonzalez, A Andres, PG Bascoy… - arxiv preprint arxiv …, 2024 - arxiv.org
Sparse reward environments in reinforcement learning (RL) pose significant challenges for
exploration, often leading to inefficient or incomplete learning processes. To tackle this …
exploration, often leading to inefficient or incomplete learning processes. To tackle this …
The impact of intrinsic rewards on exploration in Reinforcement Learning
One of the open challenges in Reinforcement Learning is the hard exploration problem in
sparse reward environments. Various types of intrinsic rewards have been proposed to …
sparse reward environments. Various types of intrinsic rewards have been proposed to …
Advancing towards Safe Reinforcement Learning over Sparse Environments with Out-of-Distribution Observations: Detection and Adaptation Strategies
Safety in AI-based systems is among the highest research priorities, particularly when such
systems are deployed in real-world scenarios subject to uncertainties and unpredictable …
systems are deployed in real-world scenarios subject to uncertainties and unpredictable …
Episodic Novelty Through Temporal Distance
Exploration in sparse reward environments remains a significant challenge in reinforcement
learning, particularly in Contextual Markov Decision Processes (CMDPs), where …
learning, particularly in Contextual Markov Decision Processes (CMDPs), where …
Fostering Intrinsic Motivation in Reinforcement Learning with Pretrained Foundation Models
Exploration remains a significant challenge in reinforcement learning, especially in
environments where extrinsic rewards are sparse or non-existent. The recent rise of …
environments where extrinsic rewards are sparse or non-existent. The recent rise of …
Enhanced Generalization through Prioritization and Diversity in Self-Imitation Reinforcement Learning over Procedural Environments with Sparse Rewards
Exploration poses a fundamental challenge in Reinforcement Learning (RL) with sparse
rewards, limiting an agent's ability to learn optimal decision-making due to a lack of …
rewards, limiting an agent's ability to learn optimal decision-making due to a lack of …
Reward Specifications in Collaborative Multi-agent Learning: A Comparative Study
M Hasan, R Niyogi - Proceedings of the 39th ACM/SIGAPP Symposium …, 2024 - dl.acm.org
Reinforcement learning is a prominent learning paradigm that seeks to maximize cumulative
rewards over time. Nevertheless, some real-life problems often exhibit inherent sparsity in …
rewards over time. Nevertheless, some real-life problems often exhibit inherent sparsity in …