This should be emphasised similar to the argument in the introduction.

Policy gradient methods have shown success in learning control policies for high-dimensional dynamical systems. Their biggest downside is the amount of exploration they require before yielding high-performing policies.

Summary: This paper considers the problem of lifelong inverse reinforcement learning, where the goal is to learn a set of reward functions (from demonstrations) that can be applied to a series of tasks. The authors propose to do this by learning and continuously updating a shared latent space of reward components, which are combined with task specific coefficients to reconstruct the reward for a particular task. The derivation of the algorithm basically mirrors the Efficient Lifelong Learning Algorithm (ELLA) (citation [33]). Although ELLA was formulated for supervised learning, variants such as PG-ELLA (not cited in this paper, by Ammar et al. "Online Multi-task Learning for Policy Gradient Methods") have applied the same derivation procedure to extend the original ELLA algorithm to the reinforcement learning setting. This paper is another extension of ELLA, to the inverse reinforcement learning setting, where instead of sharing policies via a latent space, they are sharing reward functions.

Policy gradient methods have shown success in learning control policies for high-dimensional dynamical systems. Their biggest downside is the amount of exploration they require before yielding high-performing policies. In a lifelong learning setting, in which an agent is faced with multiple consecutive tasks over its lifetime, reusing information from previously seen tasks can substantially accelerate the learning of new tasks. We provide a novel method for lifelong policy gradient learning that trains lifelong function approximators directly via policy gradients, allowing the agent to benefit from accumulated knowledge throughout the entire training process. We show empirically that our algorithm learns faster and converges to better policies than single-task and lifelong learning baselines, and completely avoids catastrophic forgetting on a variety of challenging domains.

Policy search reinforcement learning (RL) allows agents to learn autonomously with limited feedback. However, such methods typically require extensive experience for successful behavior due to their tabula rasa nature. Multitask RL is an approach, which aims to reduce data requirements by allowing knowledge transfer between tasks. Although successful, current multitask learning methods suffer from scalability issues when considering large number of tasks. The main reasons behind this limitation is the reliance on centralized solutions. This paper proposes to a novel distributed multitask RL framework, improving the scalability across many different types of tasks. Our framework maps multitask RL to an instance of general consensus and develops an efficient decentralized solver. We justify the correctness of the algorithm both theoretically and empirically: we first proof an improvement of convergence speed to an order of O(1/k) with k being the number of iterations, and then show our algorithm surpassing others on multiple dynamical system benchmarks.