System Design for an Integrated Lifelong Reinforcement Learning Agent for Real-Time Strategy Games

As Artificial and Robotic Systems are increasingly deployed and relied upon for real-world applications, it is important that they exhibit the ability to continually learn and adapt in dynamically-changing environments, becoming Lifelong Learning Machines. Continual/lifelong learning (LL) involves minimizing catastrophic forgetting of old tasks while maximizing a model's capability to learn new tasks. This paper addresses the challenging lifelong reinforcement learning (L2RL) setting. Pushing the state-of-the-art forward in L2RL and making L2RL useful for practical applications requires more than developing individual L2RL algorithms; it requires making progress at the systems-level, especially research into the non-trivial problem of how to integrate multiple L2RL algorithms into a common framework. In this paper, we introduce the Lifelong Reinforcement Learning Components Framework (L2RLCF), which standardizes L2RL systems and assimilates different continual learning components (each addressing different aspects of the lifelong learning problem) into a unified system. As an instantiation of L2RLCF, we develop a standard API allowing easy integration of novel lifelong learning components. We describe a case study that demonstrates how multiple independently-developed LL components can be integrated into a single realized system. We also introduce an evaluation environment in order to measure the effect of combining various system components. Our evaluation environment employs different LL scenarios (sequences of tasks) consisting of Starcraft-2 minigames and allows for the fair, comprehensive, and quantitative comparison of different combinations of components within a challenging common evaluation environment.Comment: The Second International Conference on AIML Systems, October 12--15, 2022, Bangalore, Indi

Lifelong Reinforcement Learning On Mobile Robots

Machine learning has shown tremendous growth in the past decades, unlocking new capabilities in a variety of fields including computer vision, natural language processing, and robotic control. While the sophistication of individual problems a learning system can handle has greatly advanced, the ability of a system to extend beyond an individual problem to adapt and solve new problems has progressed more slowly. This thesis explores the problem of progressive learning. The goal is to develop methodologies that accumulate, transfer, and adapt knowledge in applied settings where the system is faced with the ambiguity and resource limitations of operating in the physical world. There are undoubtedly many challenges to designing such a system, my thesis looks at the component of this problem related to how knowledge from previous tasks can be a benefit in the domain of reinforcement learning where the agent receives rewards for positive actions. Reinforcement learning is particularly difficult when training on physical systems, like mobile robots, where repeated trials can damage the system and unrestricted exploration is often associated with safety risks. I investigate how knowledge can be efficiently accumulated and applied to future reinforcement learning problems on mobile robots in order to reduce sample complexity and enable systems to adapt to novel settings. Doing this involves mathematical models which can combine knowledge from multiple tasks, methods for restructuring optimizations and data collection to handle sequential updates, and data selection strategies that can be used to address resource limitations

Task-Agnostic Continual Reinforcement Learning: In Praise of a Simple Baseline

We study methods for task-agnostic continual reinforcement learning (TACRL). TACRL is a setting that combines the difficulties of partially-observable RL (a consequence of task agnosticism) and the difficulties of continual learning (CL), i.e., learning on a non-stationary sequence of tasks. We compare TACRL methods with their soft upper bounds prescribed by previous literature: multi-task learning (MTL) methods which do not have to deal with non-stationary data distributions, as well as task-aware methods, which are allowed to operate under full observability. We consider a previously unexplored and straightforward baseline for TACRL, replay-based recurrent RL (3RL), in which we augment an RL algorithm with recurrent mechanisms to mitigate partial observability and experience replay mechanisms for catastrophic forgetting in CL. By studying empirical performance in a sequence of RL tasks, we find surprising occurrences of 3RL matching and overcoming the MTL and task-aware soft upper bounds. We lay out hypotheses that could explain this inflection point of continual and task-agnostic learning research. Our hypotheses are empirically tested in continuous control tasks via a large-scale study of the popular multi-task and continual learning benchmark Meta-World. By analyzing different training statistics including gradient conflict, we find evidence that 3RL's outperformance stems from its ability to quickly infer how new tasks relate with the previous ones, enabling forward transfer

How to Reuse and Compose Knowledge for a Lifetime of Tasks: A Survey on Continual Learning and Functional Composition

A major goal of artificial intelligence (AI) is to create an agent capable of acquiring a general understanding of the world. Such an agent would require the ability to continually accumulate and build upon its knowledge as it encounters new experiences. Lifelong or continual learning addresses this setting, whereby an agent faces a continual stream of problems and must strive to capture the knowledge necessary for solving each new task it encounters. If the agent is capable of accumulating knowledge in some form of compositional representation, it could then selectively reuse and combine relevant pieces of knowledge to construct novel solutions. Despite the intuitive appeal of this simple idea, the literatures on lifelong learning and compositional learning have proceeded largely separately. In an effort to promote developments that bridge between the two fields, this article surveys their respective research landscapes and discusses existing and future connections between them

Continual Task Allocation in Meta-Policy Network via Sparse Prompting

How to train a generalizable meta-policy by continually learning a sequence of tasks? It is a natural human skill yet challenging to achieve by current reinforcement learning: the agent is expected to quickly adapt to new tasks (plasticity) meanwhile retaining the common knowledge from previous tasks (stability). We address it by "Continual Task Allocation via Sparse Prompting (CoTASP)", which learns over-complete dictionaries to produce sparse masks as prompts extracting a sub-network for each task from a meta-policy network. By optimizing the sub-network and prompts alternatively, CoTASP updates the meta-policy via training a task-specific policy. The dictionary is then updated to align the optimized prompts with tasks' embedding, thereby capturing their semantic correlations. Hence, relevant tasks share more neurons in the meta-policy network via similar prompts while cross-task interference causing forgetting is effectively restrained. Given a trained meta-policy with updated dictionaries, new task adaptation reduces to highly efficient sparse prompting and sub-network finetuning. In experiments, CoTASP achieves a promising plasticity-stability trade-off without storing or replaying any past tasks' experiences and outperforms existing continual and multi-task RL methods on all seen tasks, forgetting reduction, and generalization to unseen tasks.Comment: Accepted by ICML 202

The Effectiveness of World Models for Continual Reinforcement Learning

World models power some of the most efficient reinforcement learning algorithms. In this work, we showcase that they can be harnessed for continual learning - a situation when the agent faces changing environments. World models typically employ a replay buffer for training, which can be naturally extended to continual learning. We systematically study how different selective experience replay methods affect performance, forgetting, and transfer. We also provide recommendations regarding various modeling options for using world models. The best set of choices is called Continual-Dreamer, it is task-agnostic and utilizes the world model for continual exploration. Continual-Dreamer is sample efficient and outperforms state-of-the-art task-agnostic continual reinforcement learning methods on Minigrid and Minihack benchmarks.Comment: Accepted at CoLLAs 2023, 21 pages, 15 figure

Offline Experience Replay for Continual Offline Reinforcement Learning

The capability of continuously learning new skills via a sequence of pre-collected offline datasets is desired for an agent. However, consecutively learning a sequence of offline tasks likely leads to the catastrophic forgetting issue under resource-limited scenarios. In this paper, we formulate a new setting, continual offline reinforcement learning (CORL), where an agent learns a sequence of offline reinforcement learning tasks and pursues good performance on all learned tasks with a small replay buffer without exploring any of the environments of all the sequential tasks. For consistently learning on all sequential tasks, an agent requires acquiring new knowledge and meanwhile preserving old knowledge in an offline manner. To this end, we introduced continual learning algorithms and experimentally found experience replay (ER) to be the most suitable algorithm for the CORL problem. However, we observe that introducing ER into CORL encounters a new distribution shift problem: the mismatch between the experiences in the replay buffer and trajectories from the learned policy. To address such an issue, we propose a new model-based experience selection (MBES) scheme to build the replay buffer, where a transition model is learned to approximate the state distribution. This model is used to bridge the distribution bias between the replay buffer and the learned model by filtering the data from offline data that most closely resembles the learned model for storage. Moreover, in order to enhance the ability on learning new tasks, we retrofit the experience replay method with a new dual behavior cloning (DBC) architecture to avoid the disturbance of behavior-cloning loss on the Q-learning process. In general, we call our algorithm offline experience replay (OER). Extensive experiments demonstrate that our OER method outperforms SOTA baselines in widely-used Mujoco environments.Comment: 9 pages, 4 figure

Lifelong Machine Learning Of Functionally Compositional Structures

A hallmark of human intelligence is the ability to construct self-contained chunks of knowledge and reuse them in novel combinations for solving different yet structurally related problems. Learning such compositional structures has been a significant challenge for artificial systems, due to the underlying combinatorial search. To date, research into compositional learning has largely proceeded separately from work on lifelong or continual learning. This dissertation integrated these two lines of work to present a general-purpose framework for lifelong learning of functionally compositional structures. The framework separates the learning into two stages: learning how to best combine existing components to assimilate a novel problem, and learning how to adapt the set of existing components to accommodate the new problem. This separation explicitly handles the trade-off between the stability required to remember how to solve earlier tasks and the flexibility required to solve new tasks. This dissertation instantiated the framework into various supervised and reinforcement learning (RL) algorithms. Empirical evaluations on a range of supervised learning benchmarks compared the proposed algorithms against well-established techniques, and found that 1)~compositional models enable improved lifelong learning when the tasks are highly diverse by balancing the incorporation of new knowledge and the retention of past knowledge, 2)~the separation of the learning into stages permits lifelong learning of compositional knowledge, and 3)~the components learned by the proposed methods represent self-contained and reusable functions. Similar evaluations on existing and new RL benchmarks demonstrated that 1)~algorithms under the framework accelerate the discovery of high-performing policies in a variety of domains, including robotic manipulation, and 2)~these algorithms retain, and often improve, knowledge that enables them to solve tasks learned in the past. The dissertation extended one lifelong compositional RL algorithm to the nonstationary setting, where the distribution over tasks varies over time, and found that modularity permits individually tracking changes to different elements in the environment. The final contribution of this dissertation was a new benchmark for evaluating approaches to compositional RL, which exposed that existing methods struggle to discover the compositional properties of the environment