Optimal sequential decision making with changing action space

A method for machine learning, comprising:

• receiving, by a monitoring component, state information of a decision making agent;
• selecting, by an action component, an action from an action set based on the state information using a policy neural network of the decision making agent;
• computing, by a learning component, a reward based on the action;
• computing, by the learning component, a topology loss that minimizes changes in a mapping between the action set and an action embedding space;
• and
• updating, by the learning component, parameters of the policy neural network based on the reward and the topology loss.

An apparatus for machine learning, comprising:

• a monitoring component configured to receive state information indicating a state of an environment;
• a policy neural network configured to compute an action vector from an action embedding space based on the state information, wherein the policy neural network is trained using reinforcement learning based on a topology loss that constrains changes in a mapping between an action set and the action embedding space;
• and
• an action component configured to perform an action that modifies the state based on the action vector, wherein the action is selected based on the mapping.