17617313
Using Eligibility Traces to Find the Best Memoryless Policy in Partially Observable Markov Decision Processes
Recent research on hidden-state reinforcement learning (RL) problems has concentrated on overcoming partial observability by using memory to estimate state. However, such methods are computationally extremely expensive and thus have very limited applicability. This emphasis on state estimation has come about because it has been widely observed that the presence of hidden state or partial observability renders popular RL methods such as Q-learning and Sarsa useless. However, this observation is misleading in two ways: first, the theoretical results supporting it only apply to RL algorithms that do not use eligibility traces, and second these results are worst-case results, which leaves open the possibility that there may be large classes of hidden-state problems in which RL algorithms work well without any state estimation. In this paper we show empirically that Sarsa(λ), a well known family of RL algorithms that use eligibility traces, can work very well on hidden state problems that have good memoryless policies, i.e., on RL problems in which there may well be very poor observability but there also exists a mapping from immediate observations to actions that yields near-optimal return. We apply conventional Sarsa(λ) to four test problems taken from the recent work of Littman, Littman Cassandra and Kaelbling, Parr and Russell, and Chrisman, and in each case we show that it is able to find the best, or a very good, memoryless policy without any of the computational expense of state estimation.
2624439
Learning Predictive State Representations
We introduce the first algorithm for learning predictive state representations (PSRs), which are a way of representing the state of a controlled dynamical system. The state representation in a PSR is a vector of predictions of tests, where tests are sequences of actions and observations said to be true if and only if all the observations occur given that all the actions are taken. The problem of finding a good PSR. one that is a sufficient statistic for the dynamical system--can be divided into two parts: 1) discovery of a good set of tests, and 2) learning to make accurate predictions for those tests. In this paper, we present detailed empirical results using a gradient-based algorithm for addressing the second problem. Our results demonstrate several sample systems in which the algorithm learns to make correct predictions and several situations in which the algorithm is less successful. Our analysis reveals challenges that will need to be addressed in future PSR learning algorithms.
2695116
Near-Optimal Reinforcement Learning in Polynomial Time
We present new algorithms for reinforcement learning and prove that they have polynomial bounds on the resources required to achieve near-optimal return in general Markov decision processes. After observing that the number of actions required to approach the optimal return is lower bounded by the mixing time T of the optimal policy (in the undiscounted case) or by the horizon time T (in the discounted case), we then give algorithms requiring a number of actions and total computation time that are only polynomial in T and the number of states and actions, for both the undiscounted and discounted cases. An interesting aspect of our algorithms is their explicit handling of the Exploration-Exploitation trade-off.
7241207
Intrinsically Motivated Reinforcement Learning
Psychologists call behavior intrinsically motivated when it is engaged in for its own sake rather than as a step toward solving a specific problem of clear practical value. But what we learn during intrinsically motivated behavior is essential for our development as competent autonomous entities able to efficiently solve a wide range of practical problems as they arise. In this paper we present initial results from a computational study of intrinsically motivated reinforcement learning aimed at allowing artificial agents to construct and extend hierarchies of reusable skills that are needed for competent autonomy.
5053575
Bias-Variance Error Bounds for Temporal Difference Updates
Temporal difference (td) algorithms are used in reinforcement learning to compute estimates of the value of a given policy in an unknown Markov decision process (policy evaluation). We give rigorous upper bounds on the error of the closely related phased td algorithms (which differ from the standard updates in their treatment of the learning rate) as a function of the amount of experience. These upper bounds prove exponentially fast convergence, with both the rate of convergence and the asymptote strongly dependent on the length of the backups k or the parameter . Our bounds give formal verification to the well-known intuition that td methods are subject to a bias-variance tradeoff, and they lead to schedules for k and that are predicted to be better than any fixed values for these parameters. We give preliminary experimental confirmation of our theory for a version of the random walk problem.
2661414
Graphical Models for Game Theory
We introduce a compact graph-theoretic representation for multi-party game theory. Our main result is a provably correct and efficient algorithm for computing approximate Nash equilibria in one-stage games represented by trees or sparse graphs.
7775956
Mining Invariants in Biological Sequences
2391025
Preliminary results in range-only localization and mapping
This paper presents methods of localization using cooperating landmarks (beacons) that provide the ability to measure range only. Recent advances in radio frequency technology make it possible to measure range between inexpensive beacons and a transponder Such a method has tremendous benefit since line of sight is not required between the beacons and the transponder and because the data association problem can be completely avoided. If the positions of the beacons are known, measurements from multiple beacons can be combined using probability grids to provide an accurate estimate of robot location. This estimate can be improved by using Monte Carlo techniques and Kalman filters to incorporate odometry data. Similar methods can be used to solve the simultaneous localization and mapping problem (SLAM) when beacon locations are uncertain. Experimental results are presented for robot localization. Tracking and SLAM algorithms are demonstrated in simulation.
1850582
On Scalable Attack Detection in the Network
Current intrusion detection and prevention systems seek to detect a wide class of network intrusions (e.g., DoS attacks, worms, port scans) at network vantage points. Unfortunately, even today, many IDS systems we know of keep per-connection or per-flow state to detect malicious TCP flows. Thus, it is hardly surprising that these IDS systems have not scaled to multi-gigabit speeds. By contrast, both router lookups and fair queuing have scaled to high speeds using aggregation via prefix lookups or DiffServ. Thus, in this paper, we initiate research into the question as to whether one can detect attacks without keeping per-flow state. We will show that such aggregation, while making fast implementations possible, immediately causes two problems. First, aggregation can cause behavioral aliasing where, for example, good behaviors can aggregate to look like bad behaviors. Second, aggregated schemes are susceptible to spoofing by which the intruder sends attacks that have appropriate aggregate behavior. We examine a wide variety of DoS and scanning attacks and show that several categories (bandwidth based, claim-and-hold, port-scanning) can be scalably detected. In addition to existing approaches for scalable attack detection, we propose a novel data structure called partial completion filters (PCFs) that can detect claim-and-hold attacks scalably in the network. We analyze PCFs both analytically and using experiments on real network traces to demonstrate how we can tune PCFs to achieve extremely low false positive and false negative probabilities
668272
First Results in the Coordination of Heterogeneous Robots for Large-Scale Assembly
While many multi-robot systems rely on fortuitous cooperation between agents, some tasks, such as the assembly of large structures, require tighter coordination. We present a general software architecture for coordinating heterogeneous robots that allows for both autonomy of the individual agents as well as explicit coordination. This paper presents recent results with three robots with very different configurations. Working as a team, these robots are able to perform a high-precision docking task that none could achieve individually.
748558
Automated Worm Fingerprinting
Network worms are a clear and growing threat to the security of today's Internet-connected hosts and networks. The combination of the Internet's unrestricted connectivity and widespread software homogeneity allows network pathogens to exploit tremendous parallelism in their propagation. In fact, modern worms can spread so quickly, and so widely, that no human-mediated reaction can hope to contain an outbreak. 
 
In this paper, we propose an automated approach for quickly detecting previously unknown worms and viruses based on two key behavioral characteristics - a common exploit sequence together with a range of unique sources generating infections and destinations being targeted. More importantly, our approach - called "content sifting" - automatically generates precise signatures that can then be used to filter or moderate the spread of the worm elsewhere in the network. 
 
Using a combination of existing and novel algorithms we have developed a scalable content sifting implementation with low memory and CPU requirements. Over months of active use at UCSD, our Earlybird prototype system has automatically detected and generated signatures for all pathogens known to be active on our network as well as for several new worms and viruses which were unknown at the time our system identified them. Our initial experience suggests that, for a wide range of network pathogens, it may be practical to construct fully automated defenses - even against so-called "zero-day" epidemics.