Matteo Tomasetto, Nicolò Botteghi, Gabriele Bruni +1 more
Reinforcement learning has become an appealing way to control systems too nonlinear or messy for classical methods. Its cost is appetite: the algorithms are sample inefficient, needing an enormous number of interactions before they arrive at a decent control strategy.
That appetite decides where the method can be used. Every extra sensor and actuator enlarges the space the agent has to explore, and the exploration-exploitation problem grows with it, so applications end up confined to systems with only a handful of sensing and actuation points. The curse of dimensionality is not an abstraction here, it is the reason a promising controller stays in simulation.
The move in this work is to give the learner physics rather than making it rediscover physics from scratch. A system already obeying known dynamics does not need those dynamics inferred from millions of trials, and every constraint supplied in advance is a slice of search space the agent never has to pay to explore.
Reinforcement learning (RL) has recently emerged as a promising feedback control strategy for nonlinear and complex dynamical systems. However, RL algorithms are sample inefficient and require a large number of interaction with the environment to synthesize optimal control strategies. Consequently, applications of RL are typically limited to sparse sensors and actuators due to the curse of dimensionality entailed by the exploration-exploitation dilemma in high-dimensional…
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