What are the most common statistical computing techniques used for deep reinforcement learning?

1 Data preprocessing

Data preprocessing is the process of transforming raw data into a suitable format for DRL. Data preprocessing can include data cleaning, normalization, feature extraction, dimensionality reduction, and augmentation. Data preprocessing can help to improve the quality, efficiency, and robustness of DRL by reducing noise, outliers, redundancy, and irrelevant information, as well as enhancing diversity, representation, and generalization. For example, data preprocessing can help to reduce the curse of dimensionality, which refers to the difficulty of learning from high-dimensional data with limited samples.

2 Policy gradient methods

Policy gradient methods are a class of DRL algorithms that directly optimize the policy, which is the function that maps states to actions. Policy gradient methods update the policy parameters by following the gradient of the expected return, which is the discounted sum of future rewards. Policy gradient methods can handle continuous and stochastic action spaces, as well as complex and nonlinear policies. However, policy gradient methods also suffer from high variance, low sample efficiency, and local optima. For example, policy gradient methods can be sensitive to the choice of the learning rate, which controls the step size of the gradient update.

4 Actor-critic methods

Actor-critic methods are a hybrid of policy gradient and value-based methods that combine the advantages of both approaches. Actor-critic methods consist of two components: an actor that learns the policy by following the policy gradient, and a critic that learns the value function by minimizing the TD error. The critic provides feedback to the actor by estimating the policy gradient or the advantage function, which is the difference between the value and the baseline. Actor-critic methods can balance between variance and bias, as well as exploration and exploitation. However, actor-critic methods also introduce complexity and instability due to the interaction and coordination of the two components. For example, actor-critic methods can diverge or oscillate if the actor and the critic are not compatible or synchronized.

5 Monte Carlo methods

Monte Carlo methods are a family of statistical computing techniques that use random sampling to approximate complex and intractable functions or distributions. Monte Carlo methods can be applied to DRL in various ways, such as estimating the value function, the policy gradient, the advantage function, or the posterior distribution. Monte Carlo methods can handle nonlinearity, uncertainty, and multimodality, as well as provide confidence intervals and error bounds. However, Monte Carlo methods also require a large number of samples, which can be computationally expensive and impractical. For example, Monte Carlo methods can be inefficient or inaccurate if the target function or distribution has a high variance or a low probability.

6 Bayesian methods

Bayesian methods are a set of statistical computing techniques that use Bayes' theorem to update the beliefs about the unknown parameters or models based on the observed data and the prior knowledge. Bayesian methods can be applied to DRL in various ways, such as learning the policy, the value function, the model, or the hyperparameters. Bayesian methods can incorporate uncertainty, regularize overfitting, and facilitate exploration. However, Bayesian methods also involve complex and intractable calculations, which often require approximation or optimization methods. For example, Bayesian methods can be computationally intensive or sensitive to the choice of the prior or the likelihood functions.