Methods and apparatus to automatically tune reinforcement learning hyperparameters associated with agent behavior

An apparatus, comprising:

• a memory;
• and
• a hardware processor operatively coupled to the memory, the hardware processor configured to:
• determine a first measure of performance associated with interactions of an agent with an environment, the first measure of performance being based on the agent's interactions with the environment according to a first policy;
• determine a second measure of performance associated with interactions of the agent with the environment, the second measure of performance being based on the agent's interactions with the environment according to a second policy different than the first policy;
• calculate a difference between the first measure of performance and the second measure of performance;
• and
• automatically change, based on the difference between the first measure of performance and the second measure of performance, at least one current value from a set of current values, each current value from the set of current values being associated with a different hyperparameter from a plurality of hyperparameters, the plurality of hyperparameters being configured to impact the agent's interactions with the environment.

A non-transitory processor-readable medium storing code representing instructions to be executed by a processor, the instructions comprising code to cause the processor to:

• receive data associated with interactions between a first agent and a first environment, the data including a context associated with the first environment;
• receive information about a second environment, the information including a goal that is desired to be achieved in the second environment;
• implement, using a machine learning model, a second agent configured to interact with the second environment according to a policy;
• receive information associated with interactions of the second agent with the second environment, the information including a context of the second environment and one or more measures of performance associated with the interactions of the second agent with the second environment;
• and
• modify the policy, based on the data associated with the interactions between the first agent and the first environment, by changing at least one current value from a set of current values, each current value from the set of current values being associated with a different hyperparameter from a plurality of hyperparameters, the plurality of hyperparameters being configured to impact the second agent's interactions with the second environment.