What are the most important hyperparameters to tune for a reinforcement learning algorithm in robot control?

1 Learning rate

The learning rate is a hyperparameter that determines how quickly the algorithm updates its estimates of the value function or the policy based on the observed rewards and transitions. A high learning rate means that the algorithm gives more weight to the recent experiences, while a low learning rate means that the algorithm relies more on the past knowledge. The optimal learning rate depends on the stability and complexity of the environment, as well as the amount of data available. A too high learning rate can lead to instability and oscillation, while a too low learning rate can result in slow convergence and suboptimal solutions.

2 Exploration rate

The exploration rate is a hyperparameter that controls the trade-off between exploration and exploitation in the algorithm. Exploration means that the algorithm tries new actions that may lead to higher rewards in the future, while exploitation means that the algorithm chooses the actions that have the highest estimated value based on the current knowledge. The exploration rate can be constant, decay over time, or depend on the uncertainty of the value function or the policy. The optimal exploration rate depends on the dynamics and uncertainty of the environment, as well as the learning objective. A too high exploration rate can lead to inefficient learning and wasted resources, while a too low exploration rate can lead to premature convergence and local optima.

3 Discount factor

The discount factor is a hyperparameter that determines how much the algorithm values the future rewards compared to the immediate rewards. A high discount factor means that the algorithm considers the long-term consequences of its actions, while a low discount factor means that the algorithm focuses on the short-term gains. The optimal discount factor depends on the horizon and the consistency of the reward function, as well as the risk and preference of the agent. A too high discount factor can lead to delayed gratification and unrealistic expectations, while a too low discount factor can lead to myopic and greedy behavior.

4 Network architecture

The network architecture is a hyperparameter that defines the structure and size of the neural network that represents the value function or the policy in the algorithm. The network architecture can vary in terms of the number of layers, the number of units, the activation functions, the regularization methods, and the initialization schemes. The optimal network architecture depends on the complexity and dimensionality of the state and action spaces, as well as the generalization and scalability of the algorithm. A too complex network architecture can lead to overfitting and high computational cost, while a too simple network architecture can lead to underfitting and poor performance.