The challenge of building neural networks that can continuously learn and adapt to evolving data streams is central to the fields of continual learning (CL) and reinforcement learning (RL). This lifelong learning problem is often framed in terms of the plasticity-stability dilemma, focusing on issues like loss of plasticity and catastrophic forgetting. Unlike neural networks, biological brains maintain plasticity through capacity growth, inspiring researchers to explore similar approaches in artificial networks, such as adding capacity dynamically. Prior solutions often lack parameter efficiency or depend on explicit task indices, but Mixture-of-Experts (MoE) architectures offer a promising alternative by specializing experts for distinct distributions. This paper aims to evaluate a DynamicMoE approach for continual and reinforcement learning environments and benchmark its effectiveness against existing network expansion methods.

Resource utilization is one of the key factors in fully exploiting the computing power of massively parallel devices, including GPUs. As a common method to improve utilization and reduce overhead, the benefit of the batching technique should never be underestimated [7, 8, 11]. In most cases, it is handy to batch regular workloads that share the same type and size, which also have similar amounts of computation and memory access. For example, in the CUDA programming model, this kind of regular workloads can be conveniently batched along an additional thread block or grid dimension [15]. However, irregular workloads do not naturally fit into this scheme. Irregular workloads may show one or more of the following characteristics that prevent regular batching[1]: variable amounts of computation, special memory access patterns, control flow divergence, etc. Moreover, heterogeneous workloads almost raise the difficulty of batching to an unreachable level. Here, by heterogeneous, we refer to workloads of different types of operations, e.g., some of the workloads are reduction, while others are element-wise operations. Irregular workloads are often managed in a task-parallel fashion instead of batching, where an individual workload is regarded as a task, and all tasks are dynamically scheduled [1, 19].

Decision Transformers (DT) have demonstrated strong performances in offline reinforcement learning settings, but quickly adapting to unseen novel tasks remains challenging. To address this challenge, we propose a new framework, called Hyper-Decision Transformer (HDT), that can generalize to novel tasks from a handful of demonstrations in a data-and parameter-efficient manner. To achieve such a goal, we propose to augment the base DT with an adaptation module, whose parameters are initialized by a hyper-network. When encountering unseen tasks, the hyper-network takes a handful of demonstrations as inputs and initializes the adaptation module accordingly. This initialization enables HDT to efficiently adapt to novel tasks by only fine-tuning the adaptation module. We validate HDT's generalization capability on object manipulation tasks. We find that with a single expert demonstration and fine-tuning only 0.5% of DT parameters, HDT adapts faster to unseen tasks than fine-tuning the whole DT model. Finally, we explore a more challenging setting where expert actions are not available, and we show that HDT outperforms state-of-the-art baselines in terms of task success rates by a large margin. Demos are available on our project page. Building an autonomous agent capable of generalizing to novel tasks has been a longstanding goal of artificial intelligence. Recently, large transformer models have shown strong generalization capability on language understanding when fine-tuned with limited data (Brown et al., 2020; Wei et al., 2021). Such success motivates researchers to apply transformer models to the regime of offline reinforcement learning (RL) (Chen et al., 2021; Janner et al., 2021).