21,717 research outputs found
Planning with Information-Processing Constraints and Model Uncertainty in Markov Decision Processes
Information-theoretic principles for learning and acting have been proposed
to solve particular classes of Markov Decision Problems. Mathematically, such
approaches are governed by a variational free energy principle and allow
solving MDP planning problems with information-processing constraints expressed
in terms of a Kullback-Leibler divergence with respect to a reference
distribution. Here we consider a generalization of such MDP planners by taking
model uncertainty into account. As model uncertainty can also be formalized as
an information-processing constraint, we can derive a unified solution from a
single generalized variational principle. We provide a generalized value
iteration scheme together with a convergence proof. As limit cases, this
generalized scheme includes standard value iteration with a known model,
Bayesian MDP planning, and robust planning. We demonstrate the benefits of this
approach in a grid world simulation.Comment: 16 pages, 3 figure
Solving Factored MDPs with Hybrid State and Action Variables
Efficient representations and solutions for large decision problems with
continuous and discrete variables are among the most important challenges faced
by the designers of automated decision support systems. In this paper, we
describe a novel hybrid factored Markov decision process (MDP) model that
allows for a compact representation of these problems, and a new hybrid
approximate linear programming (HALP) framework that permits their efficient
solutions. The central idea of HALP is to approximate the optimal value
function by a linear combination of basis functions and optimize its weights by
linear programming. We analyze both theoretical and computational aspects of
this approach, and demonstrate its scale-up potential on several hybrid
optimization problems
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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
Sensor Scheduling for Energy-Efficient Target Tracking in Sensor Networks
In this paper we study the problem of tracking an object moving randomly
through a network of wireless sensors. Our objective is to devise strategies
for scheduling the sensors to optimize the tradeoff between tracking
performance and energy consumption. We cast the scheduling problem as a
Partially Observable Markov Decision Process (POMDP), where the control actions
correspond to the set of sensors to activate at each time step. Using a
bottom-up approach, we consider different sensing, motion and cost models with
increasing levels of difficulty. At the first level, the sensing regions of the
different sensors do not overlap and the target is only observed within the
sensing range of an active sensor. Then, we consider sensors with overlapping
sensing range such that the tracking error, and hence the actions of the
different sensors, are tightly coupled. Finally, we consider scenarios wherein
the target locations and sensors' observations assume values on continuous
spaces. Exact solutions are generally intractable even for the simplest models
due to the dimensionality of the information and action spaces. Hence, we
devise approximate solution techniques, and in some cases derive lower bounds
on the optimal tradeoff curves. The generated scheduling policies, albeit
suboptimal, often provide close-to-optimal energy-tracking tradeoffs
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