Experience Augmentation: Boosting and Accelerating Off-Policy Multi-Agent Reinforcement Learning

• URL: http://arxiv.org/abs/2005.09453v2
• Date: Wed, 20 May 2020 02:12:08 GMT
• Title: Experience Augmentation: Boosting and Accelerating Off-Policy Multi-Agent Reinforcement Learning
• Authors: Zhenhui Ye, Yining Chen, Guanghua Song, Bowei Yang, Shen Fan
• Abstract summary: We present Experience Augmentation, which enables a time-efficient and boosted learning based on a fast, fair and thorough exploration to the environment. We demonstrate our approach by combining it with MADDPG and verifing the performance in two homogeneous and one heterogeneous environments.
• Score: 6.374722265790792
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Exploration of the high-dimensional state action space is one of the biggest challenges in Reinforcement Learning (RL), especially in multi-agent domain. We present a novel technique called Experience Augmentation, which enables a time-efficient and boosted learning based on a fast, fair and thorough exploration to the environment. It can be combined with arbitrary off-policy MARL algorithms and is applicable to either homogeneous or heterogeneous environments. We demonstrate our approach by combining it with MADDPG and verifing the performance in two homogeneous and one heterogeneous environments. In the best performing scenario, the MADDPG with experience augmentation reaches to the convergence reward of vanilla MADDPG with 1/4 realistic time, and its convergence beats the original model by a significant margin. Our ablation studies show that experience augmentation is a crucial ingredient which accelerates the training process and boosts the convergence.

Related papers

• Imagine, Initialize, and Explore: An Effective Exploration Method in Multi-Agent Reinforcement Learning [27.81925751697255]
  We propose a novel method for efficient multi-agent exploration in complex scenarios. We formulate the imagination as a sequence modeling problem, where the states, observations, prompts, actions, and rewards are predicted autoregressively. By initializing agents at the critical states, IIE significantly increases the likelihood of discovering potentially important underexplored regions.
  arXiv  Detail & Related papers  (2024-02-28T01:45:01Z)
• Curiosity & Entropy Driven Unsupervised RL in Multiple Environments [0.0]
  We propose and experiment with five new modifications to the original work. In high-dimensional environments, curiosity-driven exploration enhances learning by encouraging the agent to seek diverse experiences and explore the unknown more. However, its benefits are limited in low-dimensional and simpler environments where exploration possibilities are constrained and there is little that is truly unknown to the agent.
  arXiv  Detail & Related papers  (2024-01-08T19:25:40Z)
• Learning Better with Less: Effective Augmentation for Sample-Efficient Visual Reinforcement Learning [57.83232242068982]
  Data augmentation (DA) is a crucial technique for enhancing the sample efficiency of visual reinforcement learning (RL) algorithms. It remains unclear which attributes of DA account for its effectiveness in achieving sample-efficient visual RL. This work conducts comprehensive experiments to assess the impact of DA's attributes on its efficacy.
  arXiv  Detail & Related papers  (2023-05-25T15:46:20Z)
• SVDE: Scalable Value-Decomposition Exploration for Cooperative Multi-Agent Reinforcement Learning [22.389803019100423]
  We propose a scalable value-decomposition exploration (SVDE) method, which includes a scalable training mechanism, intrinsic reward design, and explorative experience replay. Our method achieves the best performance on almost all maps compared to other popular algorithms in a set of StarCraft II micromanagement games.
  arXiv  Detail & Related papers  (2023-03-16T03:17:20Z)
• Deep Multi-Agent Reinforcement Learning with Hybrid Action Spaces based on Maximum Entropy [0.0]
  We propose Deep Multi-Agent Hybrid Soft Actor-Critic (MAHSAC) to handle multi-agent problems with hybrid action spaces. This algorithm follows the centralized training but decentralized execution (CTDE) paradigm, and extend the Soft Actor-Critic algorithm (SAC) to handle hybrid action space problems. Our experiences are running on an easy multi-agent particle world with a continuous observation and discrete action space, along with some basic simulated physics.
  arXiv  Detail & Related papers  (2022-06-10T13:52:59Z)
• Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation [93.52573037053449]
  H-MARL (Hallucinated Multi-Agent Reinforcement Learning) learns successful equilibrium policies after a few interactions with the environment. We demonstrate our approach experimentally on an autonomous driving simulation benchmark.
  arXiv  Detail & Related papers  (2022-03-14T17:24:03Z)
• Efficiently Training On-Policy Actor-Critic Networks in Robotic Deep Reinforcement Learning with Demonstration-like Sampled Exploration [7.930709072852582]
  We propose a generic framework for Learning from Demonstration (LfD) based on actor-critic algorithms. We conduct experiments on 4 standard benchmark environments in Mujoco and 2 self-designed robotic environments.
  arXiv  Detail & Related papers  (2021-09-27T12:42:05Z)
• Locality Matters: A Scalable Value Decomposition Approach for Cooperative Multi-Agent Reinforcement Learning [52.7873574425376]
  Cooperative multi-agent reinforcement learning (MARL) faces significant scalability issues due to state and action spaces that are exponentially large in the number of agents. We propose a novel, value-based multi-agent algorithm called LOMAQ, which incorporates local rewards in the Training Decentralized Execution paradigm.
  arXiv  Detail & Related papers  (2021-09-22T10:08:15Z)
• Softmax with Regularization: Better Value Estimation in Multi-Agent Reinforcement Learning [72.28520951105207]
  Overestimation in $Q$-learning is an important problem that has been extensively studied in single-agent reinforcement learning. We propose a novel regularization-based update scheme that penalizes large joint action-values deviating from a baseline. We show that our method provides a consistent performance improvement on a set of challenging StarCraft II micromanagement tasks.
  arXiv  Detail & Related papers  (2021-03-22T14:18:39Z)
• Demonstration-efficient Inverse Reinforcement Learning in Procedurally Generated Environments [137.86426963572214]
  Inverse Reinforcement Learning can extrapolate reward functions from expert demonstrations. We show that our approach, DE-AIRL, is demonstration-efficient and still able to extrapolate reward functions which generalize to the fully procedural domain.
  arXiv  Detail & Related papers  (2020-12-04T11:18:02Z)
• Forgetful Experience Replay in Hierarchical Reinforcement Learning from Demonstrations [55.41644538483948]
  In this paper, we propose a combination of approaches that allow the agent to use low-quality demonstrations in complex vision-based environments. Our proposed goal-oriented structuring of replay buffer allows the agent to automatically highlight sub-goals for solving complex hierarchical tasks in demonstrations. The solution based on our algorithm beats all the solutions for the famous MineRL competition and allows the agent to mine a diamond in the Minecraft environment.
  arXiv  Detail & Related papers  (2020-06-17T15:38:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.