Search Results for author:
Found 5 papers, 3 papers with code
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 informative feedback signals.
Using Offline Data to Speed-up Reinforcement Learning in Procedurally Generated Environments
Motivated by the recent success of Offline RL and Imitation Learning (IL), we conduct a study to investigate whether agents can leverage offline data in the form of trajectories to improve the sample-efficiency in procedurally generated environments.
Towards Improving Exploration in Self-Imitation Learning using Intrinsic Motivation
Unfortunately, in a broad range of problems the design of a good reward function is not trivial, so in such cases sparse reward signals are instead adopted.
An Evaluation Study of Intrinsic Motivation Techniques applied to Reinforcement Learning over Hard Exploration Environments
In the last few years, the research activity around reinforcement learning tasks formulated over environments with sparse rewards has been especially notable.
Collaborative Training of Heterogeneous Reinforcement Learning Agents in Environments with Sparse Rewards: What and When to Share?
In this work we combine ideas from intrinsic motivation and transfer learning.
