Meta-Reinforcement Learning for Heuristic Planning
This work addresses a specific bottleneck in meta-RL for researchers, offering an incremental improvement in task selection methodology.
The paper tackles the problem of selecting training tasks in meta-reinforcement learning to improve learning efficiency and effectiveness, proposing an information-theoretic task selection algorithm (ITTS) that enhances final performance in reproduced experiments.
In Meta-Reinforcement Learning (meta-RL) an agent is trained on a set of tasks to prepare for and learn faster in new, unseen, but related tasks. The training tasks are usually hand-crafted to be representative of the expected distribution of test tasks and hence all used in training. We show that given a set of training tasks, learning can be both faster and more effective (leading to better performance in the test tasks), if the training tasks are appropriately selected. We propose a task selection algorithm, Information-Theoretic Task Selection (ITTS), based on information theory, which optimizes the set of tasks used for training in meta-RL, irrespectively of how they are generated. The algorithm establishes which training tasks are both sufficiently relevant for the test tasks, and different enough from one another. We reproduce different meta-RL experiments from the literature and show that ITTS improves the final performance in all of them.