Learning Dynamic Games for Cooperative Iterative Tasks
We propose a general learning framework applicable to nonlinear and nonconvex dynamic games that uses previously executed feasible trajectories to construct a sampled joint safe set and player-specific cost-to-go functions.
ProofPaper ↗
Key points
- We consider cooperative and iterative tasks involving multiple agents whose interactions are modeled as a dynamic game with shared constraints.
- At each time step, the agents jointly select a previously sampled terminal state and a Generalized Nash equilibrium used to reach it.
- A new solution is adopted only if it satisfies a cooperative acceptance rule agreed upon by the players.
- We show that the proposed framework guarantees recursive feasibility and characterize conditions under which each player's cost is non-increasing across iterations.
Sources (1)
- [1]Learning Dynamic Games for Cooperative Iterative TasksarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 08:29 PM
We propose a general learning framework applicable to nonlinear and nonconvex dynamic games that uses previously executed feasible trajectories to construct a sampled joint safe set and player-specific cost-to-go functions.
We consider cooperative and iterative tasks involving multiple agents whose interactions are modeled as a dynamic game with shared constraints.
Extractive summary: sentences quoted from the sources.
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