34,348 research outputs found
Parallel Monte Carlo Tree Search with Batched Rigid-body Simulations for Speeding up Long-Horizon Episodic Robot Planning
We propose a novel Parallel Monte Carlo tree search with Batched Simulations
(PMBS) algorithm for accelerating long-horizon, episodic robotic planning
tasks. Monte Carlo tree search (MCTS) is an effective heuristic search
algorithm for solving episodic decision-making problems whose underlying search
spaces are expansive. Leveraging a GPU-based large-scale simulator, PMBS
introduces massive parallelism into MCTS for solving planning tasks through the
batched execution of a large number of concurrent simulations, which allows for
more efficient and accurate evaluations of the expected cost-to-go over large
action spaces. When applied to the challenging manipulation tasks of object
retrieval from clutter, PMBS achieves a speedup of over with an
improved solution quality, in comparison to a serial MCTS implementation. We
show that PMBS can be directly applied to real robot hardware with negligible
sim-to-real differences. Supplementary material, including video, can be found
at https://github.com/arc-l/pmbs.Comment: Accepted for IROS 202
HyP-DESPOT: A Hybrid Parallel Algorithm for Online Planning under Uncertainty
Planning under uncertainty is critical for robust robot performance in
uncertain, dynamic environments, but it incurs high computational cost.
State-of-the-art online search algorithms, such as DESPOT, have vastly improved
the computational efficiency of planning under uncertainty and made it a
valuable tool for robotics in practice. This work takes one step further by
leveraging both CPU and GPU parallelization in order to achieve near real-time
online planning performance for complex tasks with large state, action, and
observation spaces. Specifically, we propose Hybrid Parallel DESPOT
(HyP-DESPOT), a massively parallel online planning algorithm that integrates
CPU and GPU parallelism in a multi-level scheme. It performs parallel DESPOT
tree search by simultaneously traversing multiple independent paths using
multi-core CPUs and performs parallel Monte-Carlo simulations at the leaf nodes
of the search tree using GPUs. Experimental results show that HyP-DESPOT speeds
up online planning by up to several hundred times, compared with the original
DESPOT algorithm, in several challenging robotic tasks in simulation
A Parallel Monte-Carlo Tree Search-Based Metaheuristic For Optimal Fleet Composition Considering Vehicle Routing Using Branch & Bound
In this paper, a Monte-Carlo Tree Search (MCTS)-based metaheuristic is
developed that guides a Branch & Bound (B&B) algorithm to find the globally
optimal solution to the heterogeneous fleet composition problem while
considering vehicle routing. Fleet Size and Mix Vehicle Routing Problem with
Time Windows (FSMVRPTW). The metaheuristic and exact algorithms are implemented
in a parallel hybrid optimization algorithm where the metaheuristic rapidly
finds feasible solutions that provide candidate upper bounds for the B&B
algorithm which runs simultaneously. The MCTS additionally provides a candidate
fleet composition to initiate the B&B search. Experiments show that the
proposed approach results in significant improvements in computation time and
convergence to the optimal solution.Comment: Submitted to the IEEE Intelligent Vehicles Symposium 202
Scaling Monte Carlo Tree Search on Intel Xeon Phi
Many algorithms have been parallelized successfully on the Intel Xeon Phi
coprocessor, especially those with regular, balanced, and predictable data
access patterns and instruction flows. Irregular and unbalanced algorithms are
harder to parallelize efficiently. They are, for instance, present in
artificial intelligence search algorithms such as Monte Carlo Tree Search
(MCTS). In this paper we study the scaling behavior of MCTS, on a highly
optimized real-world application, on real hardware. The Intel Xeon Phi allows
shared memory scaling studies up to 61 cores and 244 hardware threads. We
compare work-stealing (Cilk Plus and TBB) and work-sharing (FIFO scheduling)
approaches. Interestingly, we find that a straightforward thread pool with a
work-sharing FIFO queue shows the best performance. A crucial element for this
high performance is the controlling of the grain size, an approach that we call
Grain Size Controlled Parallel MCTS. Our subsequent comparing with the Xeon
CPUs shows an even more comprehensible distinction in performance between
different threading libraries. We achieve, to the best of our knowledge, the
fastest implementation of a parallel MCTS on the 61 core Intel Xeon Phi using a
real application (47 relative to a sequential run).Comment: 8 pages, 9 figure
- …