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Poster
Monte Carlo Augmented Actor-Critic for Sparse Reward Deep Reinforcement Learning from Suboptimal Demonstrations
Albert Wilcox · Ashwin Balakrishna · Jules Dedieu · Wyame Benslimane · Daniel Brown · Ken Goldberg
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 Actor-Critic (MCAC), a parameter free modification to standard actor-critic algorithms which initializes the replay buffer with demonstrations and computes a modified $Q$-value by taking the maximum of the standard temporal distance (TD) target and a Monte Carlo estimate of the reward-to-go. This encourages exploration in the neighborhood of high-performing trajectories by encouraging high $Q$-values in corresponding regions of the state space. Experiments across $5$ continuous control domains suggest that MCAC can be used to significantly increase learning efficiency across $6$ commonly used RL and RL-from-demonstrations algorithms. See https://sites.google.com/view/mcac-rl for code and supplementary material.
Author Information
Albert Wilcox (UC Berkeley)
I am an undergraduate at UC Berkeley. I do research in robotic automation for Professor Ken Goldberg's AUTOLab.
Ashwin Balakrishna (Nuro)
I am a Senior Research Scientist at Nuro working on using ideas from reinforcement and imitation learning for autonomous delivery. I just wrapped up my PhD in Computer Science at the AUTOLAB in UC Berkeley where I developed a number of algorithms for safe and efficient robot learning and studied applications to deformable manipulation, industrial automation, and robot grasping. I did my bachelors degree at Caltech in Electrical Engineering, where I worked on a number of applications of machine learning and signal processing to scientific problems, primarily in biomedical devices.
Jules Dedieu
Wyame Benslimane
Daniel Brown (University of Utah)
Ken Goldberg (UC Berkeley)
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