The Societal Implications of Deep Reinforcement Learning

A deep reinforcement learning (DRL) model is a combination of RL and DL, where RL helps the network learn which actions to take based on the inputs or rewards, and DL will scale it for more complex environments.

Machine learning (ML) can handle many complex tasks than just output singular decisions based on a labelled training dataset. Reinforcement learning (RL), a subset of ML, can train an agent to learn through interaction with the environment and use trial and error methods to alter its behaviour based on feedback. While the RL model can make decisions using a large table, modern RL applications are far too complex for the tabular approach to suffice. Deep Learning (DL), another subset of ML, can come in handy here: It uses a large matrix of numerical values to produce an output through the repeated application of mathematical operati

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