Curriculum learning in reinforcement learning is a training methodology that seeks to speed up learning of a difficult target task, by first training on a series of simpler tasks and transferring the knowledge acquired to the target task. Automatically choosing a sequence of such tasks (i.e. a curriculum) is an open problem that has been the subject of much recent work in this area. In this paper, we build upon a recent method for curriculum design, which formulates the curriculum sequencing problem as a Markov Decision Process. We extend this model to handle multiple transfer learning algorithms, and show for the first time that a curriculum policy over this MDP can be learned from experience. We explore various representations that make this possible, and evaluate our approach by learning curriculum policies for multiple agents in two different domains. The results show that our method produces curricula that can train agents to perform on a target task as fast or faster than existing methods.
A popular approach to solving large probabilistic systems relies on aggregating states based on a measure of similarity. Many approaches in the literature are heuristic. A number of recent methods rely instead on metrics based on the notion of bisimulation, or behavioral equivalence between states (, 2001, 2003; , 2004). An integral component of such metrics is the Kantorovich metric between probability distributions. However, while this metric enables many satisfying theoretical properties, it is costly to compute in practice. In this paper, we use techniques from network optimization and statistical sampling to overcome this problem. We obtain in this manner a variety of distance functions for MDP state aggregation, which differ in the tradeoff between time and space complexity, as well as the quality of the aggregation. We provide an empirical evaluation of these trade-offs. In a reinforcement learning setting, the goal of transfer learning is to improve performance on a target task by re-using knowledge from one or more source tasks. A key problem in transfer learning is how to choose appropriate source tasks for a given target task. Current approaches typically require that the agent has some experience in the target domain, or that the target task is specified by a model (e.g., a Markov Decision Process) with known parameters. To address these limitations, this paper proposes a framework for selecting source tasks in the absence of a known model or target task samples. Instead, our approach uses meta-data (e.g., attribute-value pairs) associated with each task to learn the expected benefit of transfer given a source-target task pair. To test the method, we conducted a large-scale experiment in the Ms. Pac-Man domain in which an agent played over 170 million games spanning 192 variations of the task. The agent used vast amounts of experience about transfer learning in the domain to model the benefit (or detriment) of transferring knowledge from one task to another. Subsequently, the agent successfully selected appropriate source tasks for previously unseen target tasks. Knowledge transfer between tasks can improve the performance of learned models, but requires an accurate estimate of the inter-task relationships to identify the relevant knowledge to transfer. These inter-task relationships are typically estimated based on training data for each task, which is inefficient in lifelong learning settings where the goal is to learn each consecutive task rapidly from as little data as possible. To reduce this burden, we develop a lifelong reinforcement learning method based on coupled dictionary learning that incorporates high-level task descriptors to model the intertask relationships. We show that using task descriptors improves the performance of the learned task policies, providing both theoretical justification for the benefit and empirical demonstration of the improvement across a variety of dynamical control problems. Given only the descriptor for a new task, the lifelong learner is also able to accurately predict the task policy through zero-shot learning using the coupled dictionary, eliminating the need to pause to gather training data before addressing the task. Transfer learning can improve the reinforcement learning of a new task by allowing the agent to reuse knowledge acquired from other source tasks. Despite their success, transfer learning methods rely on having relevant source tasks; transfer from inappropriate tasks can inhibit performance on the new task. For fully autonomous transfer, it is critical to have a method for automatically choosing relevant source tasks, which requires a similarity measure between Markov Decision Processes (MDPs). This issue has received little attention, and is therefore still a largely open problem. This paper presents a data-driven automated similarity measure for MDPs. This novel measure is a significant step toward autonomous reinforcement learning transfer, allowing agents to: (1) characterize when transfer will be useful and, (2) automatically select tasks to use for transfer. The proposed measure is based on the reconstruction error of a restricted Boltzmann machine that attempts to model the behavioral dynamics of the two MDPs being compared. Empirical results illustrate that this measure is correlated with the performance of transfer and therefore can be used to identify similar source tasks for transfer learning.