Learning from Demonstration without Demonstrations

State-of-the-art reinforcement learning (RL) algorithms suffer from high sample complexity, particularly in the sparse reward case. A popular strategy for mitigating this problem is to learn control policies by imitating a set of expert demonstrations. The drawback of such approaches is that an expert needs to produce demonstrations, which may be costly in practice. To address this shortcoming, we propose Probabilistic Planning for Demonstration Discovery (P2D2), a technique for automatically discovering demonstrations without access to an expert. We formulate discovering demonstrations as a search problem and leverage widely-used planning algorithms such as Rapidly-exploring Random Tree to find demonstration trajectories. These demonstrations are used to initialize a policy, then refined by a generic RL algorithm. We provide theoretical guarantees of P2D2 finding successful trajectories, as well as bounds for its sampling complexity. We experimentally demonstrate the method outperforms classic and intrinsic exploration RL techniques in a range of classic control and robotics tasks, requiring only a fraction of exploration samples and achieving better asymptotic performance.

Authors

• Tom Blau
• Philippe Morere
• Gilad Francis

Keywords

• Planing
• Reinforcement learning
• Games
• Probabilistic logic
• Search problems
• Planning
• Complexity theory
• Inverse Reinforcement Learning
• Reinforcement Learning Algorithm
• Theoretical Guarantees
• Robotic Tasks
• Search Problem
• Asymptotic Performance
• Successional Trajectories
• Rapidly-exploring Random Tree
• Expert Demonstrations
• Straight Line
• Supervised Learning
• State Space
• First Quartile
• Random Walk
• Finite Time
• Optimal Policy
• Path Planning
• Pendulum
• Reward Function
• Markov Decision Process
• Policy Learning
• Minimum Return
• Reinforcement Learning Methods
• Region Of Parameter Space
• Transition Dynamics
• Random Exploration