This is the Linux app named mapcn whose latest release can be downloaded as mapcnsourcecode.zip. It can be run online in the free hosting provider OnWorks for workstations.
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mapcn
DESCRIPTION
mapcn is a research-oriented project centered on mapping continuous control in reinforcement learning to structured policies using neural networks. It explores how high-dimensional action spaces can be decomposed into structured primitives that can be learned, composed, and reused across different tasks. The core idea is to enable agents to generalize learned behavior by representing continuous control policies in a compact, interpretable form that preserves smoothness and controllability. The project includes implementations that experiment with policy encoding, action decomposition techniques, and sample efficiency analysis in classic reinforcement learning environments. By organizing action outputs into meaningful, lower-dimensional manifolds, MapCN attempts to improve both learning convergence and transfer performance when compared to unguided continuous control baselines.
Features
- Structured continuous control policy representations
- Action decomposition into reusable low-dimensional primitives
- Training pipelines for reinforcement learning benchmarks
- Evaluation scripts for sample efficiency and performance
- Neural network policy encoders with manifold constraints
- Research-oriented codebase for continuous control exploration
Programming Language
TypeScript
Categories
This is an application that can also be fetched from https://sourceforge.net/projects/mapcn.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.
