Sokoban
30 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Sokoban
Libraries
Use these libraries to find Sokoban models and implementationsMost implemented papers
Tree Search vs Optimization Approaches for Map Generation
We compare them on three different game level generation problems: Binary, Zelda, and Sokoban.
Learning to Search with MCTSnets
yashbonde/chess_lm
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ICML 2018
They are most typically solved by tree search algorithms that simulate ahead into the future, evaluate future states, and back-up those evaluations to the root of a search tree.
Scaling All-Goals Updates in Reinforcement Learning Using Convolutional Neural Networks
Being able to reach any desired location in the environment can be a valuable asset for an agent.
Inductive general game playing
This problem is central to inductive general game playing (IGGP).
Illuminating Diverse Neural Cellular Automata for Level Generation
We present a method of generating diverse collections of neural cellular automata (NCA) to design video game levels.
Levin Tree Search with Context Models
Levin Tree Search (LTS) is a search algorithm that makes use of a policy (a probability distribution over actions) and comes with a theoretical guarantee on the number of expansions before reaching a goal node, depending on the quality of the policy.
Planning in a recurrent neural network that plays Sokoban
alignmentresearch/learned-planner
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How a neural network (NN) generalizes to novel situations depends on whether it has learned to select actions heuristically or via a planning process.
Single-Agent Policy Tree Search With Guarantees
We introduce two novel tree search algorithms that use a policy to guide search.
An investigation of model-free planning
The field of reinforcement learning (RL) is facing increasingly challenging domains with combinatorial complexity.
Learning Local Forward Models on Unforgiving Games
This paper examines learning approaches for forward models based on local cell transition functions.
