29 research outputs found
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
How to Discount Deep Reinforcement Learning: Towards New Dynamic Strategies
Using deep neural nets as function approximator for reinforcement learning
tasks have recently been shown to be very powerful for solving problems
approaching real-world complexity. Using these results as a benchmark, we
discuss the role that the discount factor may play in the quality of the
learning process of a deep Q-network (DQN). When the discount factor
progressively increases up to its final value, we empirically show that it is
possible to significantly reduce the number of learning steps. When used in
conjunction with a varying learning rate, we empirically show that it
outperforms original DQN on several experiments. We relate this phenomenon with
the instabilities of neural networks when they are used in an approximate
Dynamic Programming setting. We also describe the possibility to fall within a
local optimum during the learning process, thus connecting our discussion with
the exploration/exploitation dilemma.Comment: NIPS 2015 Deep Reinforcement Learning Worksho
Min Max Generalization for Two-stage Deterministic Batch Mode Reinforcement Learning: Relaxation Schemes
We study the minmax optimization problem introduced in [22] for computing
policies for batch mode reinforcement learning in a deterministic setting.
First, we show that this problem is NP-hard. In the two-stage case, we provide
two relaxation schemes. The first relaxation scheme works by dropping some
constraints in order to obtain a problem that is solvable in polynomial time.
The second relaxation scheme, based on a Lagrangian relaxation where all
constraints are dualized, leads to a conic quadratic programming problem. We
also theoretically prove and empirically illustrate that both relaxation
schemes provide better results than those given in [22]
Benchmarking for Bayesian Reinforcement Learning
In the Bayesian Reinforcement Learning (BRL) setting, agents try to maximise
the collected rewards while interacting with their environment while using some
prior knowledge that is accessed beforehand. Many BRL algorithms have already
been proposed, but even though a few toy examples exist in the literature,
there are still no extensive or rigorous benchmarks to compare them. The paper
addresses this problem, and provides a new BRL comparison methodology along
with the corresponding open source library. In this methodology, a comparison
criterion that measures the performance of algorithms on large sets of Markov
Decision Processes (MDPs) drawn from some probability distributions is defined.
In order to enable the comparison of non-anytime algorithms, our methodology
also includes a detailed analysis of the computation time requirement of each
algorithm. Our library is released with all source code and documentation: it
includes three test problems, each of which has two different prior
distributions, and seven state-of-the-art RL algorithms. Finally, our library
is illustrated by comparing all the available algorithms and the results are
discussed.Comment: 37 page
On overfitting and asymptotic bias in batch reinforcement learning with partial observability
This paper provides an analysis of the tradeoff between asymptotic bias
(suboptimality with unlimited data) and overfitting (additional suboptimality
due to limited data) in the context of reinforcement learning with partial
observability. Our theoretical analysis formally characterizes that while
potentially increasing the asymptotic bias, a smaller state representation
decreases the risk of overfitting. This analysis relies on expressing the
quality of a state representation by bounding L1 error terms of the associated
belief states. Theoretical results are empirically illustrated when the state
representation is a truncated history of observations, both on synthetic POMDPs
and on a large-scale POMDP in the context of smartgrids, with real-world data.
Finally, similarly to known results in the fully observable setting, we also
briefly discuss and empirically illustrate how using function approximators and
adapting the discount factor may enhance the tradeoff between asymptotic bias
and overfitting in the partially observable context.Comment: Accepted at the Journal of Artificial Intelligence Research (JAIR) -
31 page
9.糖尿病患者におけるグラム陰性桿菌敗血症の2症例(第585回千葉医学会例会・第1内科教室同門会例会)
<p>Offline computation cost Vs. Performance (inaccurate case).</p
Generating informative trajectories by using bounds on the return of control policies
Abstract We propose new methods for guiding the generation of informative trajectories when solving discrete-time optimal control problems. These methods exploit recently published results that provide ways for computing bounds on the return of control policies from a set of trajectories. Keywords: reinforcement learning, optimal control, sampling strategies Introduction. Discrete-time optimal control problems arise in many fields such as finance, medicine, engineering as well as artificial intelligence. Whatever the techniques used for solving such problems, their performance is related to the amount of information available on the system dynamics and the reward function of the optimal control problem. In this paper, we consider settings in which information on the system dynamics must be inferred from trajectories and, furthermore, due to cost and time constraints, only a limited number of trajectories can be generated. We assume that a regularity structure -given in the form of Lipschitz continuity assumptions -exists on the system dynamics and the reward function. Under such assumptions, we exploit recently published methods for computing bounds on the return of control policies from a set of trajectorie