Monte Carlo Augmented Actor-Critic for Sparse Reward Deep Reinforcement Learning from Suboptimal Demonstrations. (arXiv:2210.07432v1 [cs.LG])

Providing densely shaped reward functions for RL algorithms is often
exceedingly challenging, motivating the development of RL algorithms that can
learn from easier-to-specify sparse reward functions. This sparsity poses new
exploration challenges. One common way to address this problem is using
demonstrations to provide initial signal about regions of the state space with
high rewards. However, prior RL from demonstrations algorithms introduce
significant complexity and many hyperparameters, making them hard to implement
and tune. We introduce Monte Carlo Augmented Actor …

actor-critic arxiv reinforcement reinforcement learning