7,426 research outputs found
Predictive-State Decoders: Encoding the Future into Recurrent Networks
Recurrent neural networks (RNNs) are a vital modeling technique that rely on
internal states learned indirectly by optimization of a supervised,
unsupervised, or reinforcement training loss. RNNs are used to model dynamic
processes that are characterized by underlying latent states whose form is
often unknown, precluding its analytic representation inside an RNN. In the
Predictive-State Representation (PSR) literature, latent state processes are
modeled by an internal state representation that directly models the
distribution of future observations, and most recent work in this area has
relied on explicitly representing and targeting sufficient statistics of this
probability distribution. We seek to combine the advantages of RNNs and PSRs by
augmenting existing state-of-the-art recurrent neural networks with
Predictive-State Decoders (PSDs), which add supervision to the network's
internal state representation to target predicting future observations.
Predictive-State Decoders are simple to implement and easily incorporated into
existing training pipelines via additional loss regularization. We demonstrate
the effectiveness of PSDs with experimental results in three different domains:
probabilistic filtering, Imitation Learning, and Reinforcement Learning. In
each, our method improves statistical performance of state-of-the-art recurrent
baselines and does so with fewer iterations and less data.Comment: NIPS 201
Learning for Multi-robot Cooperation in Partially Observable Stochastic Environments with Macro-actions
This paper presents a data-driven approach for multi-robot coordination in
partially-observable domains based on Decentralized Partially Observable Markov
Decision Processes (Dec-POMDPs) and macro-actions (MAs). Dec-POMDPs provide a
general framework for cooperative sequential decision making under uncertainty
and MAs allow temporally extended and asynchronous action execution. To date,
most methods assume the underlying Dec-POMDP model is known a priori or a full
simulator is available during planning time. Previous methods which aim to
address these issues suffer from local optimality and sensitivity to initial
conditions. Additionally, few hardware demonstrations involving a large team of
heterogeneous robots and with long planning horizons exist. This work addresses
these gaps by proposing an iterative sampling based Expectation-Maximization
algorithm (iSEM) to learn polices using only trajectory data containing
observations, MAs, and rewards. Our experiments show the algorithm is able to
achieve better solution quality than the state-of-the-art learning-based
methods. We implement two variants of multi-robot Search and Rescue (SAR)
domains (with and without obstacles) on hardware to demonstrate the learned
policies can effectively control a team of distributed robots to cooperate in a
partially observable stochastic environment.Comment: Accepted to the 2017 IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS 2017
Active Perception in Adversarial Scenarios using Maximum Entropy Deep Reinforcement Learning
We pose an active perception problem where an autonomous agent actively
interacts with a second agent with potentially adversarial behaviors. Given the
uncertainty in the intent of the other agent, the objective is to collect
further evidence to help discriminate potential threats. The main technical
challenges are the partial observability of the agent intent, the adversary
modeling, and the corresponding uncertainty modeling. Note that an adversary
agent may act to mislead the autonomous agent by using a deceptive strategy
that is learned from past experiences. We propose an approach that combines
belief space planning, generative adversary modeling, and maximum entropy
reinforcement learning to obtain a stochastic belief space policy. By
accounting for various adversarial behaviors in the simulation framework and
minimizing the predictability of the autonomous agent's action, the resulting
policy is more robust to unmodeled adversarial strategies. This improved
robustness is empirically shown against an adversary that adapts to and
exploits the autonomous agent's policy when compared with a standard
Chance-Constraint Partially Observable Markov Decision Process robust approach
- …