Scalable Coordinated Exploration in Concurrent Reinforcement Learning

2018-05-23 · NeurIPS 2018 12 · Maria Dimakopoulou, Ian Osband, Benjamin Van Roy

We consider a team of reinforcement learning agents that concurrently operate in a common environment, and we develop an approach to efficient coordinated exploration that is suitable for problems of practical scale. Our approach builds on seed sampling (Dimakopoulou and Van Roy, 2018) and randomized value function learning (Osband et al., 2016). We demonstrate that, for simple tabular contexts, the approach is competitive with previously proposed tabular model learning methods (Dimakopoulou and Van Roy, 2018). With a higher-dimensional problem and a neural network value function representation, the approach learns quickly with far fewer agents than alternative exploration schemes.

📄 PDF Abstract BibTeX arXiv:1805.08948

Code (1)

efancher/cs234_work_final_project

Tasks

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Coordinated Exploration in Concurrent Reinforcement Learning

2018-02-05 · ICML 2018 7 · Maria Dimakopoulou, Benjamin Van Roy

We consider a team of reinforcement learning agents that concurrently learn to operate in a common environment. We identify three properties - adaptivity, commitment, and diversity - which are necessary for efficient coo…

Diversity reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Causal Coordinated Concurrent Reinforcement Learning

2024-01-31 · Tim Tse, Isaac Chan, Zhitang Chen

In this work, we propose a novel algorithmic framework for data sharing and coordinated exploration for the purpose of learning more data-efficient and better performing policies under a concurrent reinforcement learning…

Causal Inference reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models

2015-07-03 · Bradly C. Stadie, Sergey Levine, Pieter Abbeel

Achieving efficient and scalable exploration in complex domains poses a major challenge in reinforcement learning. While Bayesian and PAC-MDP approaches to the exploration problem offer strong formal guarantees, they are…

Atari Games reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)+1

Settling Decentralized Multi-Agent Coordinated Exploration by Novelty Sharing

2024-02-03 · Haobin Jiang, Ziluo Ding, Zongqing Lu

Exploration in decentralized cooperative multi-agent reinforcement learning faces two challenges. One is that the novelty of global states is unavailable, while the novelty of local observations is biased. The other is h…

Multi-agent Reinforcement Learning

Coordinated Multi-Agent Exploration Using Shared Goals

2021-01-01 · Iou-Jen Liu, Unnat Jain, Alex Schwing

Exploration is critical for good results of deep reinforcement learning algorithms and has drawn much attention. However, existing multi-agent deep reinforcement learning algorithms still use mostly noise-based technique…

Deep Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)+3