Recent success in deep reinforcement learning for continuous control has been dominated by model-free approaches which, unlike model-based approaches, do not suffer from representational limitations in making assumptions about the world dynamics and model errors inevitable in complex domains. However, they require a lot of experiences compared to model-based approaches that are typically more sample-efficient. We propose to combine the benefits of the two approaches by presenting an integrated approach called Curious Meta-Controller. Our approach alternates adaptively between model-based and model-free control using a curiosity feedback based on the learning progress of a neural model of the dynamics in a learned latent space. We demonstrate that our approach can significantly improve the sample efficiency and achieve near-optimal performance on learning robotic reaching and grasping tasks from raw-pixel input in both dense and sparse reward settings.
More recently, a control architecture was proposed that includes an arbitrator used to switch between habitual and planning systems by choosing between an action predicted by an actor of an actor-critic model and that predicted by an inverse dynamics model @cite_6 . The arbitration is managed by the reward prediction error and favors the actor's prediction if the error at the previous timestep is below a predefined threshold. The approach does not consider imperfect model predictions and is applied to a significantly low-dimensional state space.
@cite_6
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Internal models are important when agents make decisions based on predictions of future states and their utilities. However, using internal models for planning can be time consuming. Therefore, it can be useful to use a habitual system for repetitive tasks that can be executed faster and with reduced algorithmic resources. Current evidence suggests that the brain uses both control systems, planning and habitual systems for behavioural control, which then requires an arbitration between these two systems. In our previous work [1], we proposed an Arbitrated Predictive Actor-Critic (APAC), which is a neural architecture demonstrating cooperative mechanisms of planning and habitual control systems for one step mapping. The present study tests the ability of such a model to control a simulated twojoints robotic arm during multiple reaching tasks with movement limitations that require multiple steps to solve the task. Our results show that APAC can learn the multi-step learning under various conditions. Interestingly, the APAC tends to shift from planning to habits by taking actions predicted by a habitual controller over the training time.