Joint Control Policy Update for Multi-Agent Convergence
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Solution Overview
Problem
Existing reinforcement learning systems face challenges in efficiently updating control policies for multiple agents in multiplayer games, particularly in n-player, general-sum games, as they often converge slowly or to sub-optimal solutions, and struggle with identifying equilibria due to the complexity of Nash equilibria in these scenarios.
Innovation Solution
A system that jointly updates control policies for multiple agents in parallel, using techniques to generate a mixed joint control policy that defines a distribution over pure joint control policies, allowing for efficient identification of optimal joint control policies and convergence to equilibria such as correlated or coarse correlated equilibria, which can offer higher social welfare and flexibility compared to Nash equilibria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If sequential updating of individual control policies is used, then device complexity is reduced, but convergence speed and solution optimality deteriorate
Solution Approach 1:
The patent merges the sequential updating of individual control policies into a single joint control policy that is updated collectively for all agents. This combining of separate policy updates into a unified joint policy allows the system to capture inter-agent dependencies and achieve faster convergence to optimal solutions in multiplayer games, while the modular iterative structure keeps implementation complexity manageable.
2Device complexity
If sequential updating of individual control policies is used, then device complexity is reduced, but solution optimality deteriorates
Solution Approach 1:
The patent merges the sequential updating of individual control policies into a single joint control policy that is updated collectively for all agents. This combining of separate policy updates into a unified joint policy allows the system to capture inter-agent dependencies and achieve faster convergence to optimal solutions in multiplayer games, while the modular iterative structure keeps implementation complexity manageable.
3Speed
If joint parallel updating of control policies is used, then convergence speed and solution optimality improve, but device complexity increases
Solution Approach 1:
The patent segments the joint control policy updating process into discrete iterative steps, where each iteration performs parallel updates across all agents. This segmentation allows the complex joint updating to be broken down into manageable iterations, making the implementation tractable while still achieving the benefits of parallel processing and faster convergence.
4Adaptability or versatility
If Nash equilibrium is used for n-player general-sum games, then theoretical foundation is provided, but practical applicability deteriorates due to intractability
Solution Approach 1:
The patent changes the equilibrium concept from Nash equilibrium to correlated equilibrium for n-player general-sum games. This parameter change in the solution concept allows the system to maintain a solid theoretical foundation while achieving computational tractability, as correlated equilibria can be efficiently computed and provide practical guidance for multi-agent coordination in complex game scenarios.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating control policies for controlling agents in an environment. One of the methods includes, at each of a plurality of iterations: obtaining a current joint control policy for a plurality of agents, the current joint control policy specifying a respective current control policy for each agent; and updating the current joint control policy, comprising, for each agent: generating a respective reward estimate for each of a plurality of alternate control policies that is an estimate of a reward received by the agent if the agent is controlled using the alternate control policy while the other agents are controlled using the respective current control policies; computing a best response for the agent from the respective reward estimates; and updating the respective current control policy for the agent using the best response for the agent.