Rationality based Innate-Values-driven Reinforcement Learning

• URL: http://arxiv.org/abs/2411.09160v1
• Date: Thu, 14 Nov 2024 03:28:02 GMT
• Title: Rationality based Innate-Values-driven Reinforcement Learning
• Authors: Qin Yang,
• Abstract summary: Innate values describe agents' intrinsic motivations, which reflect their inherent interests and preferences to pursue goals. It is an excellent model to describe the innate-values-driven (IV) behaviors of AI agents. This paper proposes a hierarchical reinforcement learning model -- innate-values-driven reinforcement learning model.
• Score: 1.8220718426493654
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Innate values describe agents' intrinsic motivations, which reflect their inherent interests and preferences to pursue goals and drive them to develop diverse skills satisfying their various needs. The essence of reinforcement learning (RL) is learning from interaction based on reward-driven behaviors, much like natural agents. It is an excellent model to describe the innate-values-driven (IV) behaviors of AI agents. Especially developing the awareness of the AI agent through balancing internal and external utilities based on its needs in different tasks is a crucial problem for individuals learning to support AI agents integrating human society with safety and harmony in the long term. This paper proposes a hierarchical compound intrinsic value reinforcement learning model -- innate-values-driven reinforcement learning termed IVRL to describe the complex behaviors of AI agents' interaction. We formulated the IVRL model and proposed two IVRL models: DQN and A2C. By comparing them with benchmark algorithms such as DQN, DDQN, A2C, and PPO in the Role-Playing Game (RPG) reinforcement learning test platform VIZDoom, we demonstrated that rationally organizing various individual needs can effectively achieve better performance.

Related papers

• A Nature-Inspired Colony of Artificial Intelligence System with Fast, Detailed, and Organized Learner Agents for Enhancing Diversity and Quality [0.0]
  We present an approach that builds a CNN-based colony of AI agents to serve as a single system. The proposed system impersonates the natural environment of a biological system, like an ant colony or a human colony. The evolution of fast, detailed, and organized learners in the colony of AI is achieved by introducing a unique one-to-one mapping.
  arXiv  Detail & Related papers  (2025-04-07T12:13:14Z)
• Simulating Human-like Daily Activities with Desire-driven Autonomy [25.380194192389492]
  We introduce a Desire-driven Autonomous Agent (D2A) that can enable a large language model (LLM) to autonomously propose and select tasks. At each step, the agent evaluates the value of its current state, proposes a set of candidate activities, and selects the one that best aligns with its intrinsic motivations.
  arXiv  Detail & Related papers  (2024-12-09T12:21:20Z)
• Position Paper: Agent AI Towards a Holistic Intelligence [53.35971598180146]
  We emphasize developing Agent AI -- an embodied system that integrates large foundation models into agent actions. In this paper, we propose a novel large action model to achieve embodied intelligent behavior, the Agent Foundation Model.
  arXiv  Detail & Related papers  (2024-02-28T16:09:56Z)
• Innate-Values-driven Reinforcement Learning for Cooperative Multi-Agent Systems [1.8220718426493654]
  Innate values describe agents' intrinsic motivations, which reflect their inherent interests and preferences to pursue goals. The essence of reinforcement learning (RL) is learning from interaction based on reward-driven (such as utilities) behaviors. This paper proposes a hierarchical compound value reinforcement learning model -- intrinsic-driven reinforcement learning -- to describe the complex behaviors of multi-agent interaction.
  arXiv  Detail & Related papers  (2024-01-10T22:51:10Z)
• Pangu-Agent: A Fine-Tunable Generalist Agent with Structured Reasoning [50.47568731994238]
  Key method for creating Artificial Intelligence (AI) agents is Reinforcement Learning (RL) This paper presents a general framework model for integrating and learning structured reasoning into AI agents' policies.
  arXiv  Detail & Related papers  (2023-12-22T17:57:57Z)
• DCIR: Dynamic Consistency Intrinsic Reward for Multi-Agent Reinforcement Learning [84.22561239481901]
  We propose a new approach that enables agents to learn whether their behaviors should be consistent with that of other agents. We evaluate DCIR in multiple environments including Multi-agent Particle, Google Research Football and StarCraft II Micromanagement.
  arXiv  Detail & Related papers  (2023-12-10T06:03:57Z)
• Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimation [58.21683603243387]
  We propose three auxiliary tasks with relational-temporal reasoning and integrate them into the standard Deep Learning framework. These auxiliary tasks provide additional supervision signals to infer the behavior patterns other interactive agents. Our approach achieves robust and state-of-the-art performance in terms of standard evaluation metrics.
  arXiv  Detail & Related papers  (2023-11-27T18:57:42Z)
• Learning in Cooperative Multiagent Systems Using Cognitive and Machine Models [1.0742675209112622]
  Multi-Agent Systems (MAS) are critical for many applications requiring collaboration and coordination with humans. One major challenge is the simultaneous learning and interaction of independent agents in dynamic environments. We propose three variants of Multi-Agent IBL models (MAIBL) We demonstrate that the MAIBL models exhibit faster learning and achieve better coordination in a dynamic CMOTP task with various settings of rewards compared to current MADRL models.
  arXiv  Detail & Related papers  (2023-08-18T00:39:06Z)
• Learning to Walk Autonomously via Reset-Free Quality-Diversity [73.08073762433376]
  Quality-Diversity algorithms can discover large and complex behavioural repertoires consisting of both diverse and high-performing skills. Existing QD algorithms need large numbers of evaluations as well as episodic resets, which require manual human supervision and interventions. This paper proposes Reset-Free Quality-Diversity optimization (RF-QD) as a step towards autonomous learning for robotics in open-ended environments.
  arXiv  Detail & Related papers  (2022-04-07T14:07:51Z)
• Modeling Bounded Rationality in Multi-Agent Simulations Using Rationally Inattentive Reinforcement Learning [85.86440477005523]
  We study more human-like RL agents which incorporate an established model of human-irrationality, the Rational Inattention (RI) model. RIRL models the cost of cognitive information processing using mutual information. We show that using RIRL yields a rich spectrum of new equilibrium behaviors that differ from those found under rational assumptions.
  arXiv  Detail & Related papers  (2022-01-18T20:54:00Z)
• An active inference model of collective intelligence [0.0]
  This paper posits a minimal agent-based model that simulates the relationship between local individual-level interaction and collective intelligence. Results show that stepwise cognitive transitions increase system performance by providing complementary mechanisms for alignment between agents' local and global optima.
  arXiv  Detail & Related papers  (2021-04-02T14:32:01Z)

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.