Fisher-BRC is an algorithm used for offline reinforcement learning. It is based on actor-critic methods that encourage the learned policy to stay close to the data. The algorithm uses a neural network to learn the state-action value offset term, which can help regularize the policy changes.
Actor-critic algorithm
The actor-critic algorithm is a combination of two models - an actor and a critic. The actor is responsible for taking actions in the environment, and the critic is responsible for evaluating those actions. This algorithm is used to learn a policy that maximizes expected rewards in a given environment. The rewards can be positive or negative, and the goal is to maximize positive rewards and mitigate negative ones.
Fisher-BRC algorithm
Fisher-BRC takes a different approach to actor-critic algorithms. Instead of using the critic to evaluate the policy, it is used to regularize the policy changes. This is done by parameterizing the critic as the log-behavior-policy, plus a state-action value offset term. The behavior policy generates the offline dataset, and the offset term can be learned using a neural network.
Behavior regularization
Behavior regularization is an essential aspect of the Fisher-BRC algorithm. It refers to an appropriate regularizer on the offset term. This regularizer helps to ensure that the learned policy stays close to the data. In Fisher-BRC, a gradient penalty regularizer is used for the offset term. This regularizer is equivalent to Fisher divergence regularization, which has connections to the score matching and generative energy-based model literature.
Fisher-BRC is a promising offline reinforcement learning algorithm that encourages the learned policy to stay close to the data. It uses a neural network to learn the offset term and a gradient penalty regularizer to ensure that the policy changes are appropriately regularized. This algorithm has the potential to deliver superior performance in various reinforcement learning environments.