Behaviour-conditioned Policies For Cooperative Reinforcement Learning Tasks

Abstract

The cooperation among AI systems, and between AI systems and humans is becoming increasingly important. In various real-world tasks, an agent needs to cooperate with unknown partner agent types. This requires the agent to assess the behaviour of the partner agent during a cooperative task and to adjust its own policy to support the cooperation. Deep reinforcement learning models can be trained to deliver the required functionality but are known to suffer from sample inefficiency and slow learning. However, adapting to a partner agent behaviour during the ongoing task requires ability to assess the partner agent type quickly. We suggest a method, where we synthetically produce populations of agents with different behavioural patterns together with ground truth data of their behaviour, and use this data for training a meta-learner. We additionally suggest an agent architecture, which can efficiently use the generated data and gain the meta-learning capability. When an agent is equipped w

Stats

• citations1
• S2 citations—
• github stars0
• HF likes0
• heat score2.26
• arxiv keykeurulainen2021behaviour

Related papers

• Generalized Beliefs For Cooperative AI (2022)0.00
• Developing Cooperative Policies For Multi-stage Reinforcement Learning Tasks (2022)0.00
• Multi-agent Cooperation Through Learning-aware Policy Gradients (2024)0.00
• Collaborating With Humans Without Human Data (2021)0.00
• Robustifying A Policy In Multi-agent RL With Diverse Cooperative Behaviors And Adversarial Style Sampling For Assistive Tasks (2024)0.00
• A Hierarchical Approach To Population Training For Human-ai Collaboration (2023)0.00
• Collaboration Of AI Agents Via Cooperative Multi-agent Deep Reinforcement Learning (2019)0.00
• Proagent: Building Proactive Cooperative Agents With Large Language Models (2023)12.74