Scalable Planning and Learning for Multiagent POMDPs: Extended Version

Online, sample-based planning algorithms for POMDPs have shown great promise in scaling to problems with large state spaces, but they become intractable for large action and observation spaces. This is particularly problematic in multiagent POMDPs where the action and observation space grows exponentially with the number of agents. To combat this intractability, we propose a novel scalable approach based on sample-based planning and factored value functions that exploits structure present in many multiagent settings. This approach applies not only in the planning case, but also in the Bayesian reinforcement learning setting. Experimental results show that we are able to provide high quality solutions to large multiagent planning and learning problems

Near-Optimal BRL using Optimistic Local Transitions

Model-based Bayesian Reinforcement Learning (BRL) allows a found formalization of the problem of acting optimally while facing an unknown environment, i.e., avoiding the exploration-exploitation dilemma. However, algorithms explicitly addressing BRL suffer from such a combinatorial explosion that a large body of work relies on heuristic algorithms. This paper introduces BOLT, a simple and (almost) deterministic heuristic algorithm for BRL which is optimistic about the transition function. We analyze BOLT's sample complexity, and show that under certain parameters, the algorithm is near-optimal in the Bayesian sense with high probability. Then, experimental results highlight the key differences of this method compared to previous work.Comment: ICML201

Understanding Model-Based Reinforcement Learning and its Application in Safe Reinforcement Learning

Model-based reinforcement learning algorithms have been shown to achieve successful results on various continuous control benchmarks, but the understanding of model-based methods is limited. We try to interpret how model-based method works through novel experiments on state-of-the-art algorithms with an emphasis on the model learning part. We evaluate the role of the model learning in policy optimization and propose methods to learn a more accurate model. With a better understanding of model-based reinforcement learning, we then apply model-based methods to solve safe reinforcement learning (RL) problems with near-zero violation of hard constraints throughout training. Drawing an analogy with how humans and animals learn to perform safe actions, we break down the safe RL problem into three stages. First, we train agents in a constraint-free environment to learn a performant policy for reaching high rewards, and simultaneously learn a model of the dynamics. Second, we use model-based methods to plan safe actions and train a safeguarding policy from these actions through imitation. Finally, we propose a factored framework to train an overall policy that mixes the performant policy and the safeguarding policy. This three-step curriculum ensures near-zero violation of safety constraints at all times. As an advantage of model-based method, the sample complexity required at the second and third steps of the process is significantly lower than model-free methods and can enable online safe learning. We demonstrate the effectiveness of our methods in various continuous control problems and analyze the advantages over state-of-the-art approaches

Bayesian Nonparametric Feature and Policy Learning for Decision-Making

Learning from demonstrations has gained increasing interest in the recent past, enabling an agent to learn how to make decisions by observing an experienced teacher. While many approaches have been proposed to solve this problem, there is only little work that focuses on reasoning about the observed behavior. We assume that, in many practical problems, an agent makes its decision based on latent features, indicating a certain action. Therefore, we propose a generative model for the states and actions. Inference reveals the number of features, the features, and the policies, allowing us to learn and to analyze the underlying structure of the observed behavior. Further, our approach enables prediction of actions for new states. Simulations are used to assess the performance of the algorithm based upon this model. Moreover, the problem of learning a driver's behavior is investigated, demonstrating the performance of the proposed model in a real-world scenario