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This work presents a modular and parallelizable multi-agent deepreinforcement learning framework for imbibing cooperative as well ascompetitive behaviors within autonomous vehicles. We introduce AutoDRIVEEcosystem as an enabler to develop physically accurate and graphicallyrealistic digital twins of Nigel and F1TENTH, two scaled autonomous vehicleplatforms with unique qualities and capabilities, and leverage this ecosystemto train and deploy multi-agent reinforcement learning policies. We firstinvestigate an intersection traversal problem using a set of cooperativevehicles (Nigel) that share limited state information with each other in singleas well as multi-agent learning settings using a common policy approach. Wethen investigate an adversarial head-to-head autonomous racing problem using adifferent set of vehicles (F1TENTH) in a multi-agent learning setting using anindividual policy approach. In either set of experiments, a decentralizedlearning architecture was adopted, which allowed robust training and testing ofthe approaches in stochastic environments, since the agents were mutuallyindependent and exhibited asynchronous motion behavior. The problems werefurther aggravated by providing the agents with sparse observation spaces andrequiring them to sample control commands that implicitly satisfied the imposedkinodynamic as well as safety constraints. The experimental results for bothproblem statements are reported in terms of quantitative metrics andqualitative remarks for training as well as deployment phases.

Multi-Agent Deep Reinforcement Learning for Cooperative and Competitive Autonomous Vehicles using AutoDRIVE Ecosystem