Abstract: A method for training an agent having a planning component. The method includes carrying out a plurality of control passes, and training the planning component to reduce a loss that includes, for each of a plurality of coarse-scale state transitions occurring in the control passes from a coarse-scale state to a coarse-scale successor state, an auxiliary loss that represents a deviation between a value outputted by the planning component for the coarse-scale state and the sum of a reward received for the coarse-scale state transition and at least a portion of the value of the coarse-scale successor state.

Abstract: A method for controlling a robot. The method includes receiving an indication of a target configuration to be reached from an initial configuration of the robot, determining a coarse-scale value map by value iteration, starting from an initial coarse-scale state and until the robot reaches the target configuration or a maximum number of fine-scale states has been reached, determining a fine-scale sub-goal from the coarse-scale value map, performing, by an actuator of the robot, fine-scale control actions to reach the determined fine-scale sub-goal and obtaining sensor data to determine the fine-scale states reached, starting from a current fine-scale state of the robot and until the robot reaches the determined fine-scale sub-goal, the robot transitions to a different coarse-scale state, or a maximum sequence length of the sequence of fine-scale states has been reached and determining the next coarse-scale state.

Abstract: A processor-implemented method for generating a topological order using an artificial neural network (ANN) includes receiving a set of tasks to be performed. The tasks are represented in a graph including multiple nodes connected by edges. Each node corresponds to a task in the set of tasks. A scheduling priority is assigned to each node in the graph. A next node of potential next nodes is selected according to a probability of each of the potential next nodes based on the assigned scheduling priorities and a topology of the graph. A topological order of the tasks is generated by repeating the selection of the next node.

Abstract: A method for controlling an agent. The method includes collecting training data for multiple representations of states of the agent; for every representation and using the training data, training a state encoder, a state decoder, an action encoder and an action decoder, and a transition model, shared for the representations, for latent states, and a Q function model, shared by the representations, for latent states; receiving a state of the agent in one of the representations for which a control action is to be ascertained; mapping the state to one or more latent state(s) using the state encoder for the one of the representations; determining Q values for the state(s) for a set of actions using the Q function model; selecting the control action having the best Q value from the set of actions as the control action; and controlling the agent according to the selected control action.

Abstract: A method for controlling an agent. The method includes obtaining numerical values of a first and second set of state variables, which together represent a current full state of the agent, and the numerical values of the first set of state variables represent a current partial state of the robot; determining a state value prior comprising, for potential subsequent partial states following the current partial state, an evaluation of the subsequent partial states in terms of achieving a goal to be attained by the agent; supplying an input comprising a local crop of the state value prior and the numerical values of the second set of state variables representing, together with the numerical values of the first set of state variables, the current full state to a neural network configured to output an evaluation of control actions and controlling the agent in accordance with control signals.

Abstract: A method for configuring a neural network which is designed to map measured data to one or more output variables. The method includes: transformation(s) of the measured data is/are specified which when applied to the measured data, is/are meant to induce the output variables supplied by the neural network to exhibit an invariant or equivariant behavior; at least one equation is set up which links a condition that the desired invariance or equivariance be given with the architecture of the neural network; by solving the at least one equation a feature is obtained that characterizes the desired architecture and/or a distribution of weights of the neural network in at least one location of this architecture; a neural network is configured in such a way that its architecture and/or its distribution of weights in at least one location of this architecture has/have all of the features ascertained in this way.

Abstract: Some embodiments are directed to a computer-implemented method of interacting with a physical environment according to a policy. The policy determines multiple action probabilities of respective actions based on an observable state of the physical environment. The policy includes a neural network parameterized by a set of parameters. The neural network determines the action probabilities by determining a final layer input from an observable state and applying a final layer of the neural network to the final layer input. The final layer is applied by applying a linear combination of a set of equivariant base weight matrices to the final layer input. The base weight matrices are equivariant in the sense that, for a set of multiple predefined transformations of the final layer input, each transformation causes a corresponding predefined action permutation of the base weight matrix output for the final layer input.

Abstract: A method for controlling a robot. The method includes receiving an indication of a target configuration to be reached from an initial configuration of the robot, determining a coarse-scale value map by value iteration, starting from an initial coarse-scale state and until the robot reaches the target configuration or a maximum number of fine-scale states has been reached, determining a fine-scale sub-goal from the coarse-scale value map, performing, by an actuator of the robot, fine-scale control actions to reach the determined fine-scale sub-goal and obtaining sensor data to determine the fine-scale states reached, starting from a current fine-scale state of the robot and until the robot reaches the determined fine-scale sub-goal, the robot transitions to a different coarse-scale state, or a maximum sequence length of the sequence of fine-scale states has been reached and determining the next coarse-scale state.