Attention Actor-Critic algorithm for Multi-Agent Constrained Co-operative Reinforcement Learning

• URL: http://arxiv.org/abs/2101.02349v1
• Date: Thu, 7 Jan 2021 03:21:15 GMT
• Title: Attention Actor-Critic algorithm for Multi-Agent Constrained Co-operative Reinforcement Learning
• Authors: P.Parnika, Raghuram Bharadwaj Diddigi, Sai Koti Reddy Danda and Shalabh Bhatnagar
• Abstract summary: We consider the problem of computing optimal actions for Reinforcement Learning (RL) agents in a co-operative setting. We extend this algorithm to the constrained multi-agent RL setting.
• Score: 3.296127938396392
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we consider the problem of computing optimal actions for Reinforcement Learning (RL) agents in a co-operative setting, where the objective is to optimize a common goal. However, in many real-life applications, in addition to optimizing the goal, the agents are required to satisfy certain constraints specified on their actions. Under this setting, the objective of the agents is to not only learn the actions that optimize the common objective but also meet the specified constraints. In recent times, the Actor-Critic algorithm with an attention mechanism has been successfully applied to obtain optimal actions for RL agents in multi-agent environments. In this work, we extend this algorithm to the constrained multi-agent RL setting. The idea here is that optimizing the common goal and satisfying the constraints may require different modes of attention. By incorporating different attention modes, the agents can select useful information required for optimizing the objective and satisfying the constraints separately, thereby yielding better actions. Through experiments on benchmark multi-agent environments, we show the effectiveness of our proposed algorithm.

Related papers

• From Novice to Expert: LLM Agent Policy Optimization via Step-wise Reinforcement Learning [62.54484062185869]
  We introduce StepAgent, which utilizes step-wise reward to optimize the agent's reinforcement learning process. We propose implicit-reward and inverse reinforcement learning techniques to facilitate agent reflection and policy adjustment.
  arXiv  Detail & Related papers  (2024-11-06T10:35:11Z)
• C-MORL: Multi-Objective Reinforcement Learning through Efficient Discovery of Pareto Front [9.04360155372014]
  Constrained MORL is a seamless bridge between constrained policy optimization and MORL. Our algorithm achieves more consistent and superior performances in terms of hypervolume, expected utility, and sparsity on both discrete and continuous control tasks.
  arXiv  Detail & Related papers  (2024-10-03T06:13:56Z)
• Satisficing Exploration for Deep Reinforcement Learning [26.73584163318647]
  In complex environments that approach the vastness and scale of the real world, attaining optimal performance may in fact be an entirely intractable endeavor. Recent work has leveraged tools from information theory to design agents that deliberately forgo optimal solutions in favor of sufficiently-satisfying or satisficing solutions. We extend an agent that directly represents uncertainty over the optimal value function allowing it to both bypass the need for model-based planning and to learn satisficing policies.
  arXiv  Detail & Related papers  (2024-07-16T21:28:03Z)
• Deep Pareto Reinforcement Learning for Multi-Objective Recommender Systems [60.91599969408029]
  optimizing multiple objectives simultaneously is an important task for recommendation platforms. Existing multi-objective recommender systems do not systematically consider such dynamic relationships.
  arXiv  Detail & Related papers  (2024-07-04T02:19:49Z)
• Discovering Preference Optimization Algorithms with and for Large Language Models [50.843710797024805]
  offline preference optimization is a key method for enhancing and controlling the quality of Large Language Model (LLM) outputs. We perform objective discovery to automatically discover new state-of-the-art preference optimization algorithms without (expert) human intervention. Experiments demonstrate the state-of-the-art performance of DiscoPOP, a novel algorithm that adaptively blends logistic and exponential losses.
  arXiv  Detail & Related papers  (2024-06-12T16:58:41Z)
• A Scale-Independent Multi-Objective Reinforcement Learning with Convergence Analysis [0.6091702876917281]
  Many sequential decision-making problems need optimization of different objectives which possibly conflict with each other. We develop a single-agent scale-independent multi-objective reinforcement learning on the basis of the Advantage Actor-Critic (A2C) algorithm. A convergence analysis is then done for the devised multi-objective algorithm providing a convergence-in-mean guarantee.
  arXiv  Detail & Related papers  (2023-02-08T16:38:55Z)
• Multi-agent Deep Covering Skill Discovery [50.812414209206054]
  We propose Multi-agent Deep Covering Option Discovery, which constructs the multi-agent options through minimizing the expected cover time of the multiple agents' joint state space. Also, we propose a novel framework to adopt the multi-agent options in the MARL process. We show that the proposed algorithm can effectively capture the agent interactions with the attention mechanism, successfully identify multi-agent options, and significantly outperforms prior works using single-agent options or no options.
  arXiv  Detail & Related papers  (2022-10-07T00:40:59Z)
• Provably Efficient Algorithms for Multi-Objective Competitive RL [54.22598924633369]
  We study multi-objective reinforcement learning (RL) where an agent's reward is represented as a vector. In settings where an agent competes against opponents, its performance is measured by the distance of its average return vector to a target set. We develop statistically and computationally efficient algorithms to approach the associated target set.
  arXiv  Detail & Related papers  (2021-02-05T14:26:00Z)
• Resource Aware Multifidelity Active Learning for Efficient Optimization [0.8717253904965373]
  This paper introduces the Resource Aware Active Learning (RAAL) strategy to accelerate the optimization of black box functions. The RAAL strategy optimally seeds multiple points at each allowing for a major speed up of the optimization task.
  arXiv  Detail & Related papers  (2020-07-09T10:01:32Z)
• A Distributional View on Multi-Objective Policy Optimization [24.690800846837273]
  We propose an algorithm for multi-objective reinforcement learning that enables setting desired preferences for objectives in a scale-invariant way. We show that setting different preferences in our framework allows us to trace out the space of nondominated solutions.
  arXiv  Detail & Related papers  (2020-05-15T13:02:17Z)
• Discrete Action On-Policy Learning with Action-Value Critic [72.20609919995086]
  Reinforcement learning (RL) in discrete action space is ubiquitous in real-world applications, but its complexity grows exponentially with the action-space dimension. We construct a critic to estimate action-value functions, apply it on correlated actions, and combine these critic estimated action values to control the variance of gradient estimation. These efforts result in a new discrete action on-policy RL algorithm that empirically outperforms related on-policy algorithms relying on variance control techniques.
  arXiv  Detail & Related papers  (2020-02-10T04:23:09Z)

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.