Learning Progress Driven Multi-Agent Curriculum

• URL: http://arxiv.org/abs/2205.10016v2
• Date: Mon, 19 Feb 2024 13:35:43 GMT
• Title: Learning Progress Driven Multi-Agent Curriculum
• Authors: Wenshuai Zhao, Zhiyuan Li, Joni Pajarinen
• Abstract summary: Curriculum reinforcement learning aims to speed up learning by gradually increasing the difficulty of a task. We propose self-paced MARL (SPMARL) to prioritize tasks based on textitlearning progress instead of the episode return.
• Score: 18.239527837186216
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Curriculum reinforcement learning (CRL) aims to speed up learning by gradually increasing the difficulty of a task, usually quantified by the achievable expected return. Inspired by the success of CRL in single-agent settings, a few works have attempted to apply CRL to multi-agent reinforcement learning (MARL) using the number of agents to control task difficulty. However, existing works typically use manually defined curricula such as a linear scheme. In this paper, we first apply state-of-the-art single-agent self-paced CRL to sparse reward MARL. Although with satisfying performance, we identify two potential flaws of the curriculum generated by existing reward-based CRL methods: (1) tasks with high returns may not provide informative learning signals and (2) the exacerbated credit assignment difficulty in tasks where more agents yield higher returns. Thereby, we further propose self-paced MARL (SPMARL) to prioritize tasks based on \textit{learning progress} instead of the episode return. Our method not only outperforms baselines in three challenging sparse-reward benchmarks but also converges faster than self-paced CRL.

Related papers

• Continuous Control with Coarse-to-fine Reinforcement Learning [15.585706638252441]
  We present a framework that trains RL agents to zoom-into a continuous action space in a coarse-to-fine manner. We introduce a concrete, value-based algorithm within the framework called Coarse-to-fine Q-Network (CQN) CQN robustly learns to solve real-world manipulation tasks within a few minutes of online training.
  arXiv  Detail & Related papers  (2024-07-10T16:04:08Z)
• ArCHer: Training Language Model Agents via Hierarchical Multi-Turn RL [80.10358123795946]
  We develop a framework for building multi-turn RL algorithms for fine-tuning large language models. Our framework adopts a hierarchical RL approach and runs two RL algorithms in parallel. Empirically, we find that ArCHer significantly improves efficiency and performance on agent tasks.
  arXiv  Detail & Related papers  (2024-02-29T18:45:56Z)
• RL$^3$: Boosting Meta Reinforcement Learning via RL inside RL$^2$ [12.111848705677142]
  We propose RL$3$, a hybrid approach that incorporates action-values, learned per task through traditional RL, in the inputs to meta-RL. We show that RL$3$ earns greater cumulative reward in the long term, compared to RL$2$, while maintaining data-efficiency in the short term, and generalizes better to out-of-distribution tasks.
  arXiv  Detail & Related papers  (2023-06-28T04:16:16Z)
• Train Hard, Fight Easy: Robust Meta Reinforcement Learning [78.16589993684698]
  A major challenge of reinforcement learning (RL) in real-world applications is the variation between environments, tasks or clients. Standard MRL methods optimize the average return over tasks, but often suffer from poor results in tasks of high risk or difficulty. In this work, we define a robust MRL objective with a controlled level. The data inefficiency is addressed via the novel Robust Meta RL algorithm (RoML)
  arXiv  Detail & Related papers  (2023-01-26T14:54:39Z)
• Jump-Start Reinforcement Learning [68.82380421479675]
  We present a meta algorithm that can use offline data, demonstrations, or a pre-existing policy to initialize an RL policy. In particular, we propose Jump-Start Reinforcement Learning (JSRL), an algorithm that employs two policies to solve tasks. We show via experiments that JSRL is able to significantly outperform existing imitation and reinforcement learning algorithms.
  arXiv  Detail & Related papers  (2022-04-05T17:25:22Z)
• URLB: Unsupervised Reinforcement Learning Benchmark [82.36060735454647]
  We introduce the Unsupervised Reinforcement Learning Benchmark (URLB) URLB consists of two phases: reward-free pre-training and downstream task adaptation with extrinsic rewards. We provide twelve continuous control tasks from three domains for evaluation and open-source code for eight leading unsupervised RL methods.
  arXiv  Detail & Related papers  (2021-10-28T15:07:01Z)
• Text Generation with Efficient (Soft) Q-Learning [91.47743595382758]
  Reinforcement learning (RL) offers a more flexible solution by allowing users to plug in arbitrary task metrics as reward. We introduce a new RL formulation for text generation from the soft Q-learning perspective. We apply the approach to a wide range of tasks, including learning from noisy/negative examples, adversarial attacks, and prompt generation.
  arXiv  Detail & Related papers  (2021-06-14T18:48:40Z)
• Cross-Trajectory Representation Learning for Zero-Shot Generalization in RL [21.550201956884532]
  generalize policies learned on a few tasks over a high-dimensional observation space to similar tasks not seen during training. Many promising approaches to this challenge consider RL as a process of training two functions simultaneously. We propose Cross-Trajectory Representation Learning (CTRL), a method that runs within an RL agent and conditions its encoder to recognize behavioral similarity in observations.
  arXiv  Detail & Related papers  (2021-06-04T00:43:10Z)
• Continuous Coordination As a Realistic Scenario for Lifelong Learning [6.044372319762058]
  We introduce a multi-agent lifelong learning testbed that supports both zero-shot and few-shot settings. We evaluate several recent MARL methods, and benchmark state-of-the-art LLL algorithms in limited memory and computation. We empirically show that the agents trained in our setup are able to coordinate well with unseen agents, without any additional assumptions made by previous works.
  arXiv  Detail & Related papers  (2021-03-04T18:44:03Z)
• Conservative Q-Learning for Offline Reinforcement Learning [106.05582605650932]
  We show that CQL substantially outperforms existing offline RL methods, often learning policies that attain 2-5 times higher final return. We theoretically show that CQL produces a lower bound on the value of the current policy and that it can be incorporated into a policy learning procedure with theoretical improvement guarantees.
  arXiv  Detail & Related papers  (2020-06-08T17:53:42Z)
• Self-Paced Deep Reinforcement Learning [42.467323141301826]
  Curriculum reinforcement learning (CRL) improves the learning speed and stability of an agent by exposing it to a tailored series of tasks throughout learning. Despite empirical successes, an open question in CRL is how to automatically generate a curriculum for a given reinforcement learning (RL) agent, avoiding manual design. We propose an answer by interpreting the curriculum generation as an inference problem, where distributions over tasks are progressively learned to approach the target task. This approach leads to an automatic curriculum generation, whose pace is controlled by the agent, with solid theoretical motivation and easily integrated with deep RL algorithms.
  arXiv  Detail & Related papers  (2020-04-24T15:48:07Z)

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.