Reinforcement learning is a powerful technique in the field of artificial intelligence that enables an agent to learn from reward signals while interacting with an environment. One important aspect of reinforcement learning is exploration, or the ability of the agent to try out new actions in order to discover rewarding outcomes. One method of exploration is called Generalized State-Dependent Exploration, or gSDE.
What is State-Dependent Exploration (SDE)?
State-Dependent Exploration is an exploration method for reinforcement learning that adds noise to the deterministic action taken by the agent, based on the current state. This results in a smoother and more consistent exploration behavior compared to other methods. The exploration function is defined as:
$$\mathbf{a}_{t}=\mu\left(\mathbf{s}_{t} ; \theta_{\mu}\right)+\epsilon\left(\mathbf{s}_{t} ; \theta_{\epsilon}\right)$$
where $\mu$ is the deterministic action function, $\theta_{\mu}$ are its parameters, $\epsilon$ is the exploration function, $\theta_{\epsilon}$ are its parameters, and $\mathbf{s}_{t}$ is the current state. The exploration function is drawn from a Gaussian distribution with mean zero and variance $\sigma^{2}$.
Why use SDE?
The purpose of exploration is to encourage the agent to try out new actions in order to potentially find better solutions. However, unstructured exploration, such as uniform random exploration, can be inefficient and may cause the agent to get trapped in suboptimal solutions. State-Dependent Exploration addresses this problem by adding noise to the deterministic action in a way that is related to the current state. This allows the exploration behavior to be smoother and more consistent during each episode, which can help the agent discover better solutions more efficiently.
What is gSDE?
Generalized State-Dependent Exploration, or gSDE, is an improvement to the State-Dependent Exploration method that makes two important changes:
- The exploration function parameters, $\theta_{\epsilon}$, are now sampled every $n$ steps instead of every episode. This can help the agent explore more diverse actions over time.
- The exploration function no longer depends solely on the current state, but can instead use any chosen features. In the case of gSDE, policy features, $\mathbf{z}_{\mu}\left(\mathbf{s} ; \theta_{\mathbf{z}_{\mu}}\right)$, are used as input to the exploration function. This can help the agent explore more intelligently based on its learned policy.
Overall, gSDE represents an effective and efficient method for exploration in reinforcement learning that can help agents discover better solutions more quickly.
