Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for controlling an agent that is interacting with an environment. Implementations of the system use previously learned skills to explore states of the environment to collect and store training data, which is then used to train an action selection system. The system includes a set of skill action selection subsystems, each configured to select actions for the agent to perform for a respective skill. The set of skill action selection subsystems is used to explore states of the environment to collect the training data, keeping their individual action selection policies unchanged. A scheduler neural network selects the skill neural networks to use. The action selection system is trained on the stored training data.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for selecting actions to be performed by an agent interacting with an environment to accomplish a goal. In one aspect, a method comprises: obtaining an observation characterizing a state of the environment, processing the observation using an embedding model to generate a lower-dimensional embedding of the observation, determining an auxiliary task reward based on a value of a particular dimension of the embedding, determining an overall reward based at least in part on the auxiliary task reward, and determining an update to values of multiple parameters of an action selection neural network based on the overall reward using a reinforcement learning technique.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for controlling agents. In particular, an agent can be controlled using a hierarchical controller that includes a high-level controller neural network, a mid-level controller neural network, and a low-level controller neural network.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for controlling agents using a language model neural network and a vision-language model (VLM) neural network.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for selecting actions to be performed by an agent interacting with an environment to accomplish a goal. In one aspect, a method comprises: obtaining an observation characterizing a state of the environment, processing the observation using an embedding model to generate a lower-dimensional embedding of the observation, determining an auxiliary task reward based on a value of a particular dimension of the embedding, determining an overall reward based at least in part on the auxiliary task reward, and determining an update to values of multiple parameters of an action selection neural network based on the overall reward using a reinforcement learning technique.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for controlling an agent. One of the methods includes obtaining an observation characterizing a current state of the environment and data identifying a task currently being performed by the agent; processing the observation and the data identifying the task using a high-level controller to generate a high-level probability distribution that assigns a respective probability to each of a plurality of low-level controllers; processing the observation using each of the plurality of low-level controllers to generate, for each of the plurality of low-level controllers, a respective low-level probability distribution; generating a combined probability distribution; and selecting, using the combined probability distribution, an action from the space of possible actions to be performed by the agent in response to the observation.