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Bayesian Multi-type Mean Field Multi-agent Imitation Learning

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bm3il - implemetation and modification for new paper

PyTorch implementation

Originally taken with first commit from NIPS 2020 sumbision of paper "Bayesian Multi-type Mean Field Multi-agent Imitation Learning"

buildILBerlinGAIL: the codes for the transportation environment (Berlin) buildILGymEnvsRT: the codes for the Rover Tower environment

collect_expert: the code to collect expert samples BM3IL: Bayesian multi-type mean field multi-agent imitation learning MA-DAAC: multi-agent discriminator-attention-actor-criti MA-GAIL: multi-agent generative adversarial imitation learning MTMFIL: the MA-GAIL plus the existing multi-type mean field approximation Bayesian-2-MA-DAAC: Bayesian approach for MA-DAAC with 2 samples of the discriminator parameters per agent

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