2 مستودعات
Execution of inference tasks specifically to generate paths that satisfy target end states.
Distinct from Inference Execution: Focuses on the generative process of reaching a goal, rather than simply running a trained model.
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MarkovJunior is a probabilistic programming language and constraint propagation engine designed to generate sequences based on probabilistic rules. It utilizes a pattern matching rewrite system and a probabilistic inference tool to manage state and ensure that generated runs reach defined goal states. The system distinguishes itself through the use of wave-based superposition to track possible value assignments and prune impossible states. It employs a pattern matching rewrite system to transform specific sequences of values within multi-dimensional grids into new states. The framework suppo
Generates only the execution runs that lead to a specific goal by imposing constraints on future states.
DRL-robot-navigation is a deep reinforcement learning platform and robotic simulation framework designed to train autonomous mobile robots for collision-free path planning. It uses neural network policies and physics-engine simulation environments to teach robots how to navigate toward target coordinates while avoiding obstacles. The software trains continuous control policies using twin delayed deep deterministic policy gradients over continuous state and action spaces. Training is guided by scalar reward signals derived from target proximity and obstacle avoidance distances. System compone
Directs reinforcement learning optimization through scalar feedback based on target proximity.