We illustrate the promise of ALE by developing and benchmarking domain-independent agents designed using well-established AI techniques for both reinforcement learning and planning.
#4 best model for Atari Games on Atari 2600 Centipede
Independent reinforcement learning at each level of hierarchy enables sub-policies to adapt to consequences of their actions and recover from errors.
To perform tasks specified by natural language instructions, autonomous agents need to extract semantically meaningful representations of language and map it to visual elements and actions in the environment.
To address this challenge, we propose a hybrid imitation and reinforcement learning method, with which a dialogue agent can effectively learn from its interaction with users by learning from human teaching and feedback.
#4 best model for Dialogue State Tracking on Second dialogue state tracking challenge
The goal of imitation learning is to mimic expert behavior without access to an explicit reward signal.
By enforcing a constraint on the mutual information between the observations and the discriminator's internal representation, we can effectively modulate the discriminator's accuracy and maintain useful and informative gradients.