Transfer learning significantly accelerates the reinforcement learning process by exploiting relevant knowledge from previous experiences. The problem of optimally selecting source policies during the learning process is of great importance yet challenging. There has been little theoretical analysis of this problem. In this paper, we develop an optimal online method to select source policies for reinforcement learning. This method formulates online source policy selection as a multi-armed bandit problem and augments Q-learning with policy reuse. We provide theoretical guarantees of the optimal selection process and convergence to the optimal policy. In addition, we conduct experiments on a grid-based robot navigation domain to demonstrate its efficiency and robustness by comparing to the state-of-the-art transfer learning method.
Some related works focus on multi-task learning (MTL), which is very similar to transfer learning. MTL assumes that all MDPs are drawn from the same distribution and learning is parallel on several tasks @cite_18 . In contrast, we make no assumption regarding the distribution over MDPs and concentrate on transfer learning problem. In one previous MTL work, @cite_22 represented the distribution of MDPs with a hierarchical Bayesian model. The continuously updated distribution served as a prior for rapid learning in new environments. But as mentioned in their work, their algorithm is not computationally efficient. In more recent MTL works, @cite_16 proposed a technique that involves two phases of learning to reduce sample complexity of RL. @cite_28 determined the most similar source tasks based on compliance, which can be interpreted as a sort of distance metric between tasks.
@cite_28 @cite_18 @cite_16 @cite_22
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When transferring knowledge between reinforcement learning agents with different state representations or actions, past knowledge must be efficiently mapped to novel tasks so that it aids learning. The majority of the existing approaches use pre-defined mappings provided by a domain expert. To overcome this limitation and enable autonomous transfer learning, this paper introduces a method for weighting and using multiple inter-task mappings based on a probabilistic framework. Experimental results show that the use of multiple inter-task mappings, accompanied with a probabilistic selection mechanism, can significantly boost the performance of transfer learning relative to 1) learning without transfer and 2) using a single hand-picked mapping. We especially introduce novel tasks for transfer learning in a realistic simulation of the iCub robot, demonstrating the ability of the method to select mappings in complex tasks where human intuition could not be applied to select them. The results verified the efficacy of the proposed approach in a real world and complex environment. In this work, we design and evaluate a computational learning model that enables a human-robot team to co-develop joint strategies for performing novel tasks that require coordination. The joint strategies are learned through "perturbation training," a human team-training strategy that requires team members to practice variations of a given task to help their team generalize to new variants of that task. We formally define the problem of human-robot perturbation training and develop and evaluate the first end-to-end framework for such training, which incorporates a multi-agent transfer learning algorithm, human-robot co-learning framework and communication protocol. Our transfer learning algorithm, Adaptive Perturbation Training (AdaPT), is a hybrid of transfer and reinforcement learning techniques that learns quickly and robustly for new task variants. We empirically validate the benefits of AdaPT through comparison to other hybrid reinforcement and transfer learning techniques aimed at transferring knowledge from multiple source tasks to a single target task. @PARASPLIT We also demonstrate that AdaPT's rapid learning supports live interaction between a person and a robot, during which the human-robot team trains to achieve a high level of performance for new task variants. We augment AdaPT with a co-learning framework and a computational bi-directional communication protocol so that the robot can co-train with a person during live interaction. Results from large-scale human subject experiments (n=48) indicate that AdaPT enables an agent to learn in a manner compatible with a human's own learning process, and that a robot undergoing perturbation training with a human results in a high level of team performance. Finally, we demonstrate that human-robot training using AdaPT in a simulation environment produces effective performance for a team incorporating an embodied robot partner. Transferring knowledge across a sequence of reinforcement-learning tasks is challenging, and has a number of important applications. Though there is encouraging empirical evidence that transfer can improve performance in subsequent reinforcement-learning tasks, there has been very little theoretical analysis. In this paper, we introduce a new multi-task algorithm for a sequence of reinforcement-learning tasks when each task is sampled independently from (an unknown) distribution over a finite set of Markov decision processes whose parameters are initially unknown. For this setting, we prove under certain assumptions that the per-task sample complexity of exploration is reduced significantly due to transfer compared to standard single-task algorithms. Our multi-task algorithm also has the desired characteristic that it is guaranteed not to exhibit negative transfer: in the worst case its per-task sample complexity is comparable to the corresponding single-task algorithm. We consider the problem of multi-task reinforcement learning, where the agent needs to solve a sequence of Markov Decision Processes (MDPs) chosen randomly from a fixed but unknown distribution. We model the distribution over MDPs using a hierarchical Bayesian infinite mixture model. For each novel MDP, we use the previously learned distribution as an informed prior for modelbased Bayesian reinforcement learning. The hierarchical Bayesian framework provides a strong prior that allows us to rapidly infer the characteristics of new environments based on previous environments, while the use of a nonparametric model allows us to quickly adapt to environments we have not encountered before. In addition, the use of infinite mixtures allows for the model to automatically learn the number of underlying MDP components. We evaluate our approach and show that it leads to significant speedups in convergence to an optimal policy after observing only a small number of tasks.