Reinforcement learning with inductive logic programming
AssigneeInternational Business Machines Corporation
InventorsAkifumi Wachi, Songtao Lu
Life of this patent
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Abstract
Methods and systems for training a model and automated motion include learning Markov decision processes using reinforcement learning in respective training environments. Logic rules are extracted from the Markov decision processes. T reward logic neural network (LNN) and a safety LNN are trained using the logic rules extracted from the Markov decision processes. The reward LNN and the safety LNN each take a state-action pair as an input and output a corresponding score for the state-action pair.
Claims20
A computer-implemented method for training a model, comprising:
- learning a plurality of Markov decision processes using reinforcement learning in respective training environments;
- extracting logic rules from the plurality of Markov decision processes;
- and
- training a reward logic neural network (LNN) and a safety LNN using the logic rules extracted from the plurality of Markov decision processes, wherein the reward LNN and the safety LNN each take a state-action pair as an input and output a corresponding score for the state-action pair.
A computer-implemented method for automated motion, comprising:
- determining a state of an environment using a sensor on a vehicle;
- determining a proposed action, based on the state, using a reward logic neural network (LNN) that generates a reward score based on a state-action pair;
- determining that the proposed action is safe, using a safety LNN that generates a safety score based on the state-action pair;
- and
- automatically performing the proposed action on the vehicle.
A system for automated motion, comprising:
- a sensor that collects state information about an environment;
- a driving system that performs actions in a vehicle;
- a hardware processor;
- a memory that stores a computer program, which, when executed by the hardware processor, causes the hardware processor to:
- determine a proposed action, based on the state information, using a reward logic neural network (LNN) that generates a reward score based on a state-action pair;
- determine that the proposed action is safe, using a safety LNN that generates a safety score based on the state-action pair;
- and
- automatically perform the proposed action using the driving system.
Dependencies are inferred from the claim text.