CM3: Cooperative Multi-goal Multi-stage Multi-agent Reinforcement Learning (1809.05188v3)

Abstract: A variety of cooperative multi-agent control problems require agents to achieve individual goals while contributing to collective success. This multi-goal multi-agent setting poses difficulties for recent algorithms, which primarily target settings with a single global reward, due to two new challenges: efficient exploration for learning both individual goal attainment and cooperation for others' success, and credit-assignment for interactions between actions and goals of different agents. To address both challenges, we restructure the problem into a novel two-stage curriculum, in which single-agent goal attainment is learned prior to learning multi-agent cooperation, and we derive a new multi-goal multi-agent policy gradient with a credit function for localized credit assignment. We use a function augmentation scheme to bridge value and policy functions across the curriculum. The complete architecture, called CM3, learns significantly faster than direct adaptations of existing algorithms on three challenging multi-goal multi-agent problems: cooperative navigation in difficult formations, negotiating multi-vehicle lane changes in the SUMO traffic simulator, and strategic cooperation in a Checkers environment.

• The paper introduces a two-stage curriculum that first trains agents on individual goals before progressing to cooperative multi-agent tasks.
• It employs function augmentation and a localized credit function to assign rewards accurately for action-goal pairs.
• Empirical validations demonstrate that CM3 outperforms existing methods in complex tasks such as cooperative navigation and traffic lane merging.

Cooperative Multi-goal Multi-stage Multi-agent Reinforcement Learning (CM3)

In the field of reinforcement learning, the paper on Cooperative Multi-goal Multi-stage Multi-agent Reinforcement Learning (CM3) introduces a sophisticated approach to tackle the cooperative multi-agent problems where agents aim for individual goals while contributing to optimize global success. This paper addresses two significant challenges related to multi-agent settings with multiple goals: efficient exploration strategies and precise credit-assignment mechanisms.

Challenges and Novel Approach

The complexity of multi-agent environments with distinct goals lies within two areas:

1. Exploration and Cooperation: Agents need strategies to explore efficiently such that they can achieve their own goals and assist others in achieving theirs. Uniform random exploration is inefficient and requires more nuanced approaches that take into account the necessity for cooperation in restricted regions of state space.
2. Credit-assignment: Accurately assigning credit to agents for their actions, especially when those actions influence the success of other agents in achieving their goals, is crucial. A coarse approach treating all goals as a single joint goal dilutes the ability to evaluate impact accurately.

To address these, the paper restructures the problem into a two-stage curriculum. Initially, agents learn to attain single-agent goals (Stage 1), which then primes them for multi-agent cooperation (Stage 2). The CM3 architecture introduces a multi-goal multi-agent policy gradient that utilizes a credit function for localized credit assignment, facilitating efficient learning across both stages.

Methodology

• Curriculum Learning: This approach involves a novel two-stage training regimen where agents first learn to act in a single-agent environment to achieve individual goals. Building on this foundation, agents are better equipped to explore and discover cooperative solutions in a multi-agent setup.
• Function Augmentation: The curriculum is supported by function augmentation that bridges the value and policy functions across stages. This setup reduces the number of trainable parameters initially and expands them as agents transition to the multi-agent context.
• Credit Function: The introduction of an action-value function, termed the credit function, evaluates action-goal pairs rather than pure joint actions. This function facilitates localized credit assignment, crucial for multi-goal scenarios, allowing precise policy updates based on agent interactions.

The CM3 framework is empirically validated on complex multi-goal environments such as cooperative navigation tasks, lane merging in traffic, and strategic games like Checkers. Results demonstrate that CM3 notably outperforms existing algorithms, solving complex configurations in fewer episodes.

Implications and Future Research

The CM3 framework offers several practical implications:

1. Autonomous Systems: In applications like autonomous driving or robotic coordination, the ability to learn decentralized policies that optimize individual and collective objectives simultaneously can improve efficiency and safety.
2. Scalability: CM3's architecture allows for scalable decentralized execution, indicating potential for broader implementation in environments with numerous agents and complex goals.
3. Higher Order Interactions: While the current credit function assesses first-order interactions, future research could explore higher-order interactions among agents’ actions and goals.

Theoretical analyses of CM3's properties, evaluating scenarios without pre-known goal assignments, and extending to heterogeneous agents present promising avenues for future exploration. Overall, CM3 contributes significantly to the multi-agent reinforcement learning field, providing a robust framework for tackling complex cooperative tasks across various domains.