1,308 research outputs found
Efficient Credit Assignment through Evaluation Function Decomposition
Evolutionary methods are powerful tools in discovering solutions for difficult continuous tasks. When such a solution is encoded over multiple genes, a genetic algorithm faces the difficult credit assignment problem of evaluating how a single gene in a chromosome contributes to the full solution. Typically a single evaluation function is used for the entire chromosome, implicitly giving each gene in the chromosome the same evaluation. This method is inefficient because a gene will get credit for the contribution of all the other genes as well. Accurately measuring the fitness of individual genes in such a large search space requires many trials. This paper instead proposes turning this single complex search problem into a multi-agent search problem, where each agent has the simpler task of discovering a suitable gene. Gene-specific evaluation functions can then be created that have better theoretical properties than a single evaluation function over all genes. This method is tested in the difficult double-pole balancing problem, showing that agents using gene-specific evaluation functions can create a successful control policy in 20 percent fewer trials than the best existing genetic algorithms. The method is extended to more distributed problems, achieving 95 percent performance gains over tradition methods in the multi-rover domain
Certified Reinforcement Learning with Logic Guidance
This paper proposes the first model-free Reinforcement Learning (RL)
framework to synthesise policies for unknown, and continuous-state Markov
Decision Processes (MDPs), such that a given linear temporal property is
satisfied. We convert the given property into a Limit Deterministic Buchi
Automaton (LDBA), namely a finite-state machine expressing the property.
Exploiting the structure of the LDBA, we shape a synchronous reward function
on-the-fly, so that an RL algorithm can synthesise a policy resulting in traces
that probabilistically satisfy the linear temporal property. This probability
(certificate) is also calculated in parallel with policy learning when the
state space of the MDP is finite: as such, the RL algorithm produces a policy
that is certified with respect to the property. Under the assumption of finite
state space, theoretical guarantees are provided on the convergence of the RL
algorithm to an optimal policy, maximising the above probability. We also show
that our method produces ''best available'' control policies when the logical
property cannot be satisfied. In the general case of a continuous state space,
we propose a neural network architecture for RL and we empirically show that
the algorithm finds satisfying policies, if there exist such policies. The
performance of the proposed framework is evaluated via a set of numerical
examples and benchmarks, where we observe an improvement of one order of
magnitude in the number of iterations required for the policy synthesis,
compared to existing approaches whenever available.Comment: This article draws from arXiv:1801.08099, arXiv:1809.0782
Gaussian Process Planning with Lipschitz Continuous Reward Functions: Towards Unifying Bayesian Optimization, Active Learning, and Beyond
This paper presents a novel nonmyopic adaptive Gaussian process planning
(GPP) framework endowed with a general class of Lipschitz continuous reward
functions that can unify some active learning/sensing and Bayesian optimization
criteria and offer practitioners some flexibility to specify their desired
choices for defining new tasks/problems. In particular, it utilizes a
principled Bayesian sequential decision problem framework for jointly and
naturally optimizing the exploration-exploitation trade-off. In general, the
resulting induced GPP policy cannot be derived exactly due to an uncountable
set of candidate observations. A key contribution of our work here thus lies in
exploiting the Lipschitz continuity of the reward functions to solve for a
nonmyopic adaptive epsilon-optimal GPP (epsilon-GPP) policy. To plan in real
time, we further propose an asymptotically optimal, branch-and-bound anytime
variant of epsilon-GPP with performance guarantee. We empirically demonstrate
the effectiveness of our epsilon-GPP policy and its anytime variant in Bayesian
optimization and an energy harvesting task.Comment: 30th AAAI Conference on Artificial Intelligence (AAAI 2016), Extended
version with proofs, 17 page
FPGA Accelerator Architecture for Q-learning and its Applications in Space Exploration Rovers
abstract: Achieving human level intelligence is a long-term goal for many Artificial Intelligence (AI) researchers. Recent developments in combining deep learning and reinforcement learning helped us to move a step forward in achieving this goal. Reinforcement learning using a delayed reward mechanism is an approach to machine intelligence which studies decision making with control and how a decision making agent can learn to act optimally in an environment-unaware conditions.
Q-learning is one of the model-free reinforcement directed learning strategies which uses temporal differences to estimate the performances of state-action pairs called Q values. A simple implementation of Q-learning algorithm can be done using a Q table memory to store and update the Q values. However, with an increase in state space data due to a complex environment, and with an increase in possible number of actions an agent can perform, Q table reaches its space limit and would be difficult to scale well. Q-learning with neural networks eliminates the use of Q table by approximating the Q function using neural networks.
Autonomous agents need to develop cognitive properties and become self-adaptive to be deployable in any environment. Reinforcement learning with Q-learning have been very efficient in solving such problems. However, embedded systems like space rovers and autonomous robots rarely implement such techniques due to the constraints faced like processing power, chip area, convergence rate and cost of the chip. These problems present a need for a portable, low power, area efficient hardware accelerator to accelerate the process of such learning.
This problem is targeted by implementing a hardware schematic architecture for Q-learning using Artificial Neural networks. This architecture exploits the massive parallelism provided by neural network with a dedicated fine grain parallelism provided by a Field Programmable Gate Array (FPGA) thereby processing the Q values at a high throughput. Mars exploration rovers currently use Xilinx-Space-grade FPGA devices for image processing, pyrotechnic operation control and obstacle avoidance. The hardware resource consumption for the architecture has been synthesized considering Xilinx Virtex7 FPGA as the target device.Dissertation/ThesisMasters Thesis Engineering 201
Adaptive Informative Path Planning with Multimodal Sensing
Adaptive Informative Path Planning (AIPP) problems model an agent tasked with
obtaining information subject to resource constraints in unknown, partially
observable environments. Existing work on AIPP has focused on representing
observations about the world as a result of agent movement. We formulate the
more general setting where the agent may choose between different sensors at
the cost of some energy, in addition to traversing the environment to gather
information. We call this problem AIPPMS (MS for Multimodal Sensing). AIPPMS
requires reasoning jointly about the effects of sensing and movement in terms
of both energy expended and information gained. We frame AIPPMS as a Partially
Observable Markov Decision Process (POMDP) and solve it with online planning.
Our approach is based on the Partially Observable Monte Carlo Planning
framework with modifications to ensure constraint feasibility and a heuristic
rollout policy tailored for AIPPMS. We evaluate our method on two domains: a
simulated search-and-rescue scenario and a challenging extension to the classic
RockSample problem. We find that our approach outperforms a classic AIPP
algorithm that is modified for AIPPMS, as well as online planning using a
random rollout policy.Comment: First two authors contributed equally; International Conference on
Automated Planning and Scheduling (ICAPS) 202
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