14 research outputs found
Structural Return Maximization for Reinforcement Learning
Batch Reinforcement Learning (RL) algorithms attempt to choose a policy from
a designer-provided class of policies given a fixed set of training data.
Choosing the policy which maximizes an estimate of return often leads to
over-fitting when only limited data is available, due to the size of the policy
class in relation to the amount of data available. In this work, we focus on
learning policy classes that are appropriately sized to the amount of data
available. We accomplish this by using the principle of Structural Risk
Minimization, from Statistical Learning Theory, which uses Rademacher
complexity to identify a policy class that maximizes a bound on the return of
the best policy in the chosen policy class, given the available data. Unlike
similar batch RL approaches, our bound on return requires only extremely weak
assumptions on the true system
Simultaneous Perturbation Algorithms for Batch Off-Policy Search
We propose novel policy search algorithms in the context of off-policy, batch
mode reinforcement learning (RL) with continuous state and action spaces. Given
a batch collection of trajectories, we perform off-line policy evaluation using
an algorithm similar to that by [Fonteneau et al., 2010]. Using this
Monte-Carlo like policy evaluator, we perform policy search in a class of
parameterized policies. We propose both first order policy gradient and second
order policy Newton algorithms. All our algorithms incorporate simultaneous
perturbation estimates for the gradient as well as the Hessian of the
cost-to-go vector, since the latter is unknown and only biased estimates are
available. We demonstrate their practicality on a simple 1-dimensional
continuous state space problem
Reinforcement learning with misspecified model classes
Real-world robots commonly have to act in complex, poorly understood environments where the true world dynamics are unknown. To compensate for the unknown world dynamics, we often provide a class of models to a learner so it may select a model, typically using a minimum prediction error metric over a set of training data. Often in real-world domains the model class is unable to capture the true dynamics, due to either limited domain knowledge or a desire to use a small model. In these cases we call the model class misspecified, and an unfortunate consequence of misspecification is that even with unlimited data and computation there is no guarantee the model with minimum prediction error leads to the best performing policy. In this work, our approach improves upon the standard maximum likelihood model selection metric by explicitly selecting the model which achieves the highest expected reward, rather than the most likely model. We present an algorithm for which the highest performing model from the model class is guaranteed to be found given unlimited data and computation. Empirically, we demonstrate that our algorithm is often superior to the maximum likelihood learner in a batch learning setting for two common RL benchmark problems and a third real-world system, the hydrodynamic cart-pole, a domain whose complex dynamics cannot be known exactly.United States. Office of Naval Research. Multidisciplinary University Research Initiative (N00014-11-1-0688
Efficient reinforcement learning through variance reduction and trajectory synthesis
Reinforcement learning is a general and unified framework that has been proven promising for many important AI applications, such as robotics, self-driving vehicles. However, current reinforcement learning algorithms suffer from large variance and sampling inefficiency, which leads to slow convergent rate as well as unstable performance. In this thesis, we manage to alleviate these two relevant problems. For enormous variance, we combine variance reduced optimization with deep Q-learning. For inefficient sampling, we propose novel framework that integrates self-imitation learning and artificial synthesis procedure. Our approaches, which are flexible and could be extended to many tasks, prove their effectiveness through experiments on Atari and MuJoCo environment