This paper investigates the feasibility of using Graph Neural Networks (GNNs) for classical motion planning problems. Planning algorithms that search through discrete spaces as well as continuous ones are studied. This paper proposes using GNNs to guide the search algorithm by exploiting the ability of GNNs to extract low level information about the topology of a planning space. We present two techniques, GNNs over dense fixed graphs for low-dimensional problems and sampling-based GNNs for high-dimensional problems. We examine the ability of a GNN to tackle planning problems that are heavily dependent on the topology of the space such as identifying critical nodes, learning a heuristic that guides exploration in $\text{A}^*$, and learning the sampling distribution in Rapidly-exploring Random Trees (RRT). We demonstrate that GNNs can offer better results when compared to traditional analytic methods as well as learning-based approaches that employ fully-connected networks or convolutional neural networks.

For complex real-world systems, designing controllers are a difficult task. With the advent of neural networks as a proxy for complex function approximators, it has become popular to learn the controller directly. However, these controllers are specific to a given task and need to be relearned for a new task. Alternatively, one can learn just the model of the dynamical system and compose it with external controllers. Such a model is task (and controller) agnostic and must generalize well across the state space. This paper proposes learning a "sufficiently accurate" model of the dynamics that explicitly enforces small residual error on pre-defined parts of the state-space. We formulate task agnostic controller design for this learned model as an optimization problem with state and control constraints that is solved in an online fashion. We validate this approach in simulation using a challenging contact-based Ball-Paddle system.

In this paper, we explore using deep reinforcement learning for problems with multiple agents. Most existing methods for deep multi-agent reinforcement learning consider only a small number of agents. When the number of agents increases, the dimensionality of the input and control spaces increase as well, and these methods do not scale well. To address this, we propose casting the multi-agent reinforcement learning problem as a distributed optimization problem. Our algorithm assumes that for multi-agent settings, policies of individual agents in a given population live close to each other in parameter space and can be approximated by a single policy. With this simple assumption, we show our algorithm to be extremely effective for reinforcement learning in multi-agent settings. We demonstrate its effectiveness against existing comparable approaches on co-operative and competitive tasks.

Planning problems in partially observable environments cannot be solved directly with convolutional networks and require some form of memory. But, even memory networks with sophisticated addressing schemes are unable to learn intelligent reasoning satisfactorily due to the complexity of simultaneously learning to access memory and plan. To mitigate these challenges we introduce the Memory Augmented Control Network (MACN). The proposed network architecture consists of three main parts. The first part uses convolutions to extract features and the second part uses a neural network-based planning module to pre-plan in the environment. The third part uses a network controller that learns to store those specific instances of past information that are necessary for planning. The performance of the network is evaluated in discrete grid world environments for path planning in the presence of simple and complex obstacles. We show that our network learns to plan and can generalize to new environments.