Curiosity-driven Exploration in Sparse-reward Multi-agent Reinforcement Learning
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Abstract (arXiv)
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 intrinsic motivation which advocates for adding an intrinsic reward to the reward function to encourage the agent to explore the environment and expand the sample space. Though intrinsic motivation methods are widely used to improve data-efficient learning in the reinforcement learning model, they also suffer from the so-called detachment problem. In this article, we discuss the limitations of intrinsic curiosity module in sparse-reward multi-agent reinforcement learning and propose a method called I-Go-Explore that combines the intrinsic curiosity module with the Go-Explore framework to alleviate the detachment problem.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 29 of its 66 references that are in the map. First posted on arXiv 21 Feb 2023.
Builds on
29 of 66 references.
References · 29
- 2017Proximal Policy Optimization Algorithmscited by 14,441
- 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,302
- 2015High-Dimensional Continuous Control Using Generalized Advantage Estimationcited by 1,916
- 2016Generative Adversarial Imitation Learningcited by 1,576
- 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,519
- 2017Curiosity-driven Exploration by Self-supervised Predictioncited by 1,173
- 2017Hindsight Experience Replaycited by 1,096
- 2015Prioritized Experience Replaycited by 1,070
- 2018Exploration by Random Network Distillationcited by 966
- 2021The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Gamescited by 903
- 2017FeUdal Networks for Hierarchical Reinforcement Learningcited by 328
- 2020First return, then explorecited by 256
17 more
- 2020Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Taskscited by 207
- 2020Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPOcited by 184
- 2022Exploration in Deep Reinforcement Learning: A Surveycited by 158
- 2021Cooperative Exploration for Multi-Agent Deep Reinforcement Learningcited by 57
- 2021On Improving Model-Free Algorithms for Decentralized Multi-Agent Reinforcement Learningcited by 35
- 2019Message-Dropout: An Efficient Training Method for Multi-Agent Deep Reinforcement Learningcited by 17
- 2019A New Framework for Multi-Agent Reinforcement Learning -- Centralized Training and Exploration with Decentralized Execution via Policy Distillationcited by 12
- 2020Counterfactual Multi-Agent Reinforcement Learning with Graph Convolution Communicationcited by 12
- 2020Proximal Policy Optimization via Enhanced Exploration Efficiencycited by 9
- 2020Multi-Agent Reinforcement Learning for Problems with Combined Individual and Team Rewardcited by 8
- 2022An Evaluation Study of Intrinsic Motivation Techniques applied to Reinforcement Learning over Hard Exploration Environmentscited by 8
- 2022Generative Personas That Behave and Experience Like Humanscited by 7
- 2022Play with Emotion: Affect-Driven Reinforcement Learningcited by 6
- 2022CH-MARL: A Multimodal Benchmark for Cooperative, Heterogeneous Multi-Agent Reinforcement Learningcited by 4
- 2022Go-Explore Complex 3D Game Environments for Automated Reachability Testingcited by 3
- 2022Curiosity-Driven Multi-Agent Exploration with Mixed Objectivescited by 2
- 2022First Go, then Post-Explore: the Benefits of Post-Exploration in Intrinsic Motivationcited by 1
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