Automated curriculum generation for Policy Gradients from Demonstrations

In this paper, we present a technique that improves the process of training an agent (using RL) for instruction following. We develop a training curriculum that uses a nominal number of expert demonstrations and trains the agent in a manner that draws parallels from one of the ways in which humans learn to perform complex tasks, i.e by starting from the goal and working backwards... (read more)

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METHOD TYPE
Entropy Regularization
Regularization
PPO
Policy Gradient Methods