**Understanding Reinforcement Learning Basics**
Reinforcement Learning (RL) is a field of machine learning that focuses on learning optimal decision-making policies through trial and error, based on rewards and punishments. In this article, we will explore some questions about RL basics and provide answers to help deepen your understanding of the topic.
**The Role of Loss Functions in Reinforcement Learning**
When reading RL papers, it is common to encounter references to “loss functions” that guide the training of neural networks. In RL, policy optimization algorithms like Proximal Policy Optimization (PPO) use loss functions to train the current policy by minimizing the (negative) expected return at the policy’s parameters. It is important to note that this loss function is defined with respect to data sampled by the *current* policy, rather than pre-existing datasets used in supervised learning.
**The Meaning of Loss Functions in RL**
While minimizing the loss function in RL does not directly translate to evaluating the actual performance of the policy, it still plays a crucial role in training the neural network. The decrease in the loss value indicates an improvement in the tolerance of actions within a fine-grained manipulation task, for example. However, it is challenging to determine the exact amount of decrease in loss that corresponds to an increase in reward due to non-linear sensitivity between parameters, outputs, and rewards given by the environment.
**The Use of Discount Factors in DRL**
Discount factors are frequently used in Deep Reinforcement Learning (DRL) algorithms to optimize the undiscounted return. These factors serve as important hyperparameters in tuning RL agents, as they bias the optimization landscape towards preferring immediate rewards over delayed rewards. By finishing an episode sooner, agents have the opportunity to explore more episodes, enhancing the learning algorithm’s search and exploration capabilities. Discounting also introduces a symmetry-breaking effect that reduces the search space, making tasks easier to learn.
**The Role of Planning Loops in Model-Based RL**
In model-based RL, embedding planning loops into policies helps mitigate the problem of model bias. When we have a good Q function, we can recover a policy by performing a search procedure to find the best action that results in the highest expected future returns. By using a neural network “actor” to perform amortized search over the argmax Q(s,a), we can extract information about the best action to take in a given state. However, if we have a perfect model of dynamics and an imperfect Q function, planning helps by considering the future state and querying Q(s,a) at each state in the trajectory to identify inconsistencies. This allows us to improve the Q function’s reliability even before taking any actions.
**Using Data Augmentation in Model-Free RL**
Data augmentation is a technique commonly used in model-free RL methods to enhance the agent’s learning process. It involves augmenting real experiences with fictitious ones during agent updates. If we have a perfect world model, training the agent solely on imagined rollouts is equivalent to training it on real experience. This is advantageous in robotics, where training purely in “mental simulation” eliminates the need for physical wear and tear on robots. However, since perfect world models are rarely attainable, combining real interactions with imaginary experiences provides grounding in reality for both the imagination model and policy training.
**Choosing the Baseline in Policy Gradients**
In policy gradients, choosing an appropriate baseline is crucial. The baseline, often represented by the function b, helps reduce variance in policy gradient estimators. The choice of baseline depends on the specific RL algorithm and problem at hand. Popular choices for baselines include the state-value function or the average value estimated across a batch of samples. The goal is to select a baseline that reduces the variance of the policy gradient estimator and improves the stability of the learning process.
By exploring these questions about RL basics, we have gained a deeper understanding of the intricacies involved in reinforcement learning. The role of loss functions, discount factors, planning loops, data augmentation, and choosing the right baseline are all fundamental aspects of RL research that contribute to the development of more effective algorithms and policies.