Abstract: In example embodiments, a method of interactive imitation learning method is disclosed. An input is received from an input device. The input includes data describing a first set of example actions defining a behavior for a virtual character. Inverse reinforcement learning is used to estimate a reward function for the set of example actions. The reward function and the set of example actions is used as input to a reinforcement learning model to train a machine learning agent to mimic the behavior in a training environment. Based on a measure of failure of the training of the machine learning agent reaching a threshold, the training of the machine learning agent is paused to request a second set of example actions from the input device. The second set of example actions is used in addition to the first set of example actions to estimate a new reward function.

Abstract: In example embodiments, a method of interactive imitation learning method is disclosed. An input is received from an input device. The input includes data describing a first set of example actions defining a behavior for a virtual character. Inverse reinforcement learning is used to estimate a reward function for the set of example actions. The reward function and the set of example actions is used as input to a reinforcement learning model to train a machine learning agent to mimic the behavior in a training environment. Based on a measure of failure of the training of the machine learning agent reaching a threshold, the training of the machine learning agent is paused to request a second set of example actions from the input device. The second set of example actions is used in addition to the first set of example actions to estimate a new reward function.

Abstract: In example embodiments, a method of interactive imitation learning method is disclosed. An input is received from an input device. The input includes data describing a first set of example actions defining a behavior for a virtual character. Inverse reinforcement learning is used to estimate a reward function for the set of example actions. The reward function and the set of example actions is used as input to a reinforcement learning model to train a machine learning agent to mimic the behavior in a training environment. Based on a measure of failure of the training of the machine learning agent reaching a threshold, the training of the machine learning agent is paused to request a second set of example actions from the input device. The second set of example actions is used in addition to the first set of example actions to estimate a new reward function.

Abstract: A method of behavior generation is disclosed. Planning state data in a planning domain language format is received and a state description and an associated action description based on the planning state data are generated. The state description and the associated action description are parsed into a series of tokens for a machine learning encoded state and associated ML encoded action. The series of tokens describe the state and the action. The ML encoded state and ML encoded action is processed with a recurrent neural network to generate an estimate of a value of the state description and the action description. Output of the RNN is taken as input into a neural network to generate a value estimate for a state-action pair. A plan that includes a plurality of sequential actions for an agent is generated. The plurality of sequential actions is chosen based on at least the value estimate.

Abstract: In example embodiments, a method of interactive imitation learning method is disclosed. An input is received from an input device. The input includes data describing a first set of example actions defining a behavior for a virtual character. Inverse reinforcement learning is used to estimate a reward function for the set of example actions. The reward function and the set of example actions is used as input to a reinforcement learning model to train a machine learning agent to mimic the behavior in a training environment. Based on a measure of failure of the training of the machine learning agent reaching a threshold, the training of the machine learning agent is paused to request a second set of example actions from the input device. The second set of example actions is used in addition to the first set of example actions to estimate a new reward function.