Guided Exploration with Proximal Policy Optimization using a Single Demonstration

Abstract

Solving sparse reward tasks through exploration is one of the major challenges in deep reinforcement learning, especially in three-dimensional, partially-observable environments. Critically, the algorithm proposed in this article uses a single human demonstration to solve hard-exploration problems. We train an agent on a combination of demonstrations and own experience to solve problems with variable initial conditions. We adapt this idea and integrate it with the proximal policy optimization (PPO). The agent is able to increase its performance and to tackle harder problems by replaying its own past trajectories prioritizing them based on the obtained reward and the maximum value of the trajectory. We compare different variations of this algorithm to behavioral cloning on a set of hard-exploration tasks in the Animal-AI Olympics environment. To the best of our knowledge, learning a task in a three-dimensional environment with comparable difficulty has never been considered before using only one human demonstration.

References in corpus (12)

• IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
• Emergent Tool Use From Multi-Agent Autocurricula
• Count-Based Exploration with Neural Density Models
• Deep Q-learning from Demonstrations
• Exploration by Random Network Distillation
• Agent57: Outperforming the Atari Human Benchmark
• One-Shot Imitation Learning
• Go-Explore: a New Approach for Hard-Exploration Problems
• Learning Montezuma's Revenge from a Single Demonstration
• Contingency-Aware Exploration in Reinforcement Learning
• Making Efficient Use of Demonstrations to Solve Hard Exploration Problems
• Watch, Try, Learn: Meta-Learning from Demonstrations and Reward