mapcn

We have hosted the application mapcn in order to run this application in our online workstations with Wine or directly.

Quick description about mapcn:

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.
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