103 research outputs found
Assessing the Potential of Classical Q-learning in General Game Playing
After the recent groundbreaking results of AlphaGo and AlphaZero, we have
seen strong interests in deep reinforcement learning and artificial general
intelligence (AGI) in game playing. However, deep learning is
resource-intensive and the theory is not yet well developed. For small games,
simple classical table-based Q-learning might still be the algorithm of choice.
General Game Playing (GGP) provides a good testbed for reinforcement learning
to research AGI. Q-learning is one of the canonical reinforcement learning
methods, and has been used by (Banerjee Stone, IJCAI 2007) in GGP. In this
paper we implement Q-learning in GGP for three small-board games (Tic-Tac-Toe,
Connect Four, Hex)\footnote{source code: https://github.com/wh1992v/ggp-rl}, to
allow comparison to Banerjee et al.. We find that Q-learning converges to a
high win rate in GGP. For the -greedy strategy, we propose a first
enhancement, the dynamic algorithm. In addition, inspired by (Gelly
Silver, ICML 2007) we combine online search (Monte Carlo Search) to
enhance offline learning, and propose QM-learning for GGP. Both enhancements
improve the performance of classical Q-learning. In this work, GGP allows us to
show, if augmented by appropriate enhancements, that classical table-based
Q-learning can perform well in small games.Comment: arXiv admin note: substantial text overlap with arXiv:1802.0594
Bootstrapping Monte Carlo Tree Search with an Imperfect Heuristic
We consider the problem of using a heuristic policy to improve the value
approximation by the Upper Confidence Bound applied in Trees (UCT) algorithm in
non-adversarial settings such as planning with large-state space Markov
Decision Processes. Current improvements to UCT focus on either changing the
action selection formula at the internal nodes or the rollout policy at the
leaf nodes of the search tree. In this work, we propose to add an auxiliary arm
to each of the internal nodes, and always use the heuristic policy to roll out
simulations at the auxiliary arms. The method aims to get fast convergence to
optimal values at states where the heuristic policy is optimal, while retaining
similar approximation as the original UCT in other states. We show that
bootstrapping with the proposed method in the new algorithm, UCT-Aux, performs
better compared to the original UCT algorithm and its variants in two benchmark
experiment settings. We also examine conditions under which UCT-Aux works well.Comment: 16 pages, accepted for presentation at ECML'1
Q-CP: Learning Action Values for Cooperative Planning
Research on multi-robot systems has demonstrated promising results in manifold applications and domains. Still, efficiently learning an effective robot behaviors is very difficult, due to unstructured scenarios, high uncertainties, and large state dimensionality (e.g. hyper-redundant and groups of robot). To alleviate this problem, we present Q-CP a cooperative model-based reinforcement learning algorithm, which exploits action values to both (1) guide the exploration of the state space and (2) generate effective policies. Specifically, we exploit Q-learning to attack the curse-of-dimensionality in the iterations of a Monte-Carlo Tree Search. We implement and evaluate Q-CP on different stochastic cooperative (general-sum) games: (1) a simple cooperative navigation problem among 3 robots, (2) a cooperation scenario between a pair of KUKA YouBots performing hand-overs, and (3) a coordination task between two mobile robots entering a door. The obtained results show the effectiveness of Q-CP in the chosen applications, where action values drive the exploration and reduce the computational demand of the planning process while achieving good performance
Pilot, Rollout and Monte Carlo Tree Search Methods for Job Shop Scheduling
Greedy heuristics may be attuned by looking ahead for each possible choice,
in an approach called the rollout or Pilot method. These methods may be seen as
meta-heuristics that can enhance (any) heuristic solution, by repetitively
modifying a master solution: similarly to what is done in game tree search,
better choices are identified using lookahead, based on solutions obtained by
repeatedly using a greedy heuristic. This paper first illustrates how the Pilot
method improves upon some simple well known dispatch heuristics for the
job-shop scheduling problem. The Pilot method is then shown to be a special
case of the more recent Monte Carlo Tree Search (MCTS) methods: Unlike the
Pilot method, MCTS methods use random completion of partial solutions to
identify promising branches of the tree. The Pilot method and a simple version
of MCTS, using the -greedy exploration paradigms, are then
compared within the same framework, consisting of 300 scheduling problems of
varying sizes with fixed-budget of rollouts. Results demonstrate that MCTS
reaches better or same results as the Pilot methods in this context.Comment: Learning and Intelligent OptimizatioN (LION'6) 7219 (2012
Finding Competitive Network Architectures Within a Day Using UCT
The design of neural network architectures for a new data set is a laborious
task which requires human deep learning expertise. In order to make deep
learning available for a broader audience, automated methods for finding a
neural network architecture are vital. Recently proposed methods can already
achieve human expert level performances. However, these methods have run times
of months or even years of GPU computing time, ignoring hardware constraints as
faced by many researchers and companies. We propose the use of Monte Carlo
planning in combination with two different UCT (upper confidence bound applied
to trees) derivations to search for network architectures. We adapt the UCT
algorithm to the needs of network architecture search by proposing two ways of
sharing information between different branches of the search tree. In an
empirical study we are able to demonstrate that this method is able to find
competitive networks for MNIST, SVHN and CIFAR-10 in just a single GPU day.
Extending the search time to five GPU days, we are able to outperform human
architectures and our competitors which consider the same types of layers
Biasing MCTS with Features for General Games
This paper proposes using a linear function approximator, rather than a deep
neural network (DNN), to bias a Monte Carlo tree search (MCTS) player for
general games. This is unlikely to match the potential raw playing strength of
DNNs, but has advantages in terms of generality, interpretability and resources
(time and hardware) required for training. Features describing local patterns
are used as inputs. The features are formulated in such a way that they are
easily interpretable and applicable to a wide range of general games, and might
encode simple local strategies. We gradually create new features during the
same self-play training process used to learn feature weights. We evaluate the
playing strength of an MCTS player biased by learnt features against a standard
upper confidence bounds for trees (UCT) player in multiple different board
games, and demonstrate significantly improved playing strength in the majority
of them after a small number of self-play training games.Comment: Accepted at IEEE CEC 2019, Special Session on Games. Copyright of
final version held by IEE
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