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You could change some parameters of your A2C model, perhaps the learning rate or the alpha/epsilon/gamma/momentum, for instance: model = A2C('MlpLstmPolicy', env, verbose=1, learning_rate=0.0001, alpha=0.001, momentum = 0.02) or using a different LSTM architecture, for instance: policy_kwargs = dict(net_arch=[64, 'lstm', dict(vf=[128, 128, 128], pi=[64, 64])...


Maybe a little different from your problem but this article engaged Reinforcement Learning to improve the level-of-service (LOS) for a shared-taxi system.

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