;\n }\n\n // Goal position\n if (i === goal) {\n cellClass += \" bg-yellow-200\";\n cellContent = \"🏁\";\n }\n\n // Obstacles\n if (obstacles.includes(i)) {\n cellClass += \" bg-gray-700\";\n }\n\n if (type === \"regular\") {\n // Regular RL path\n if (regularPath.includes(i) && i !== 0 && i !== goal) {\n cellClass += \" bg-blue-300\";\n if (regularPath[regularPath.length - 1] === i && step >= 6) {\n cellContent =

;\n }\n }\n } else {\n // Vers RL paths\n Object.entries(versPaths).forEach(([branch, path]) => {\n if (path.includes(i) && i !== 0 && i !== goal) {\n const lastIndex = path[path.length - 1];\n\n // Color based on branch\n const branchColors = {\n A: \"bg-blue-300\",\n B: \"bg-red-300\",\n C: \"bg-green-300\",\n D: \"bg-purple-300\",\n E: \"bg-orange-300\",\n F: \"bg-pink-300\",\n G: \"bg-indigo-300\",\n };\n\n // Only color if not already colored by another branch\n if (!cellClass.includes(\"bg-\")) {\n cellClass += ` ${branchColors[branch]}`;\n }\n\n // Show agent position at end of active paths\n if (lastIndex === i && (step < 6 || branch === \"A\")) {\n cellContent = (\n

\n );\n }\n }\n });\n }\n\n cells.push(\n

\n {cellContent}\n

,\n );\n }\n\n return (\n

\n {cells}\n

\n );\n };\n\n const renderResourceBar = (type) => {\n if (type === \"regular\") {\n return (\n

\n 100% resources\n

\n );\n } else {\n return (\n

\n {versRLStats[step].resources.map((res, idx) => {\n const branchColors = {\n \"A\": \"bg-blue-500\",\n \"B\": \"bg-red-500\",\n \"C\": \"bg-green-500\",\n \"D\": \"bg-purple-500\",\n \"E\": \"bg-orange-500\",\n \"F\": \"bg-pink-500\",\n \"G\": \"bg-indigo-500\",\n };\n\n return (\n \n

\n {res.percent > 10 ? `${res.branch}: ${res.percent}%` : \"\"}\n

\n );\n })}\n \n );\n }\n };\n\n const renderProgressBar = (current, total) => {\n const percent = (current / total) * 100;\n return (\n

\n );\n };\n\n return (\n

Regular RL vs. vers-Enhanced RL

\n This demonstration shows how traditional RL exploration compares to vers-enhanced RL in solving a complex maze\n navigation task.\n

\n\n

\n \n {playing ? \"Pause\" : \"Play\"}\n \n \n Reset\n \n

\n Speed:\n setSpeed(Number(e.target.value))}\n className=\"border rounded p-1\"\n >\n \n \n \n \n

\n Step: {step}/7\n

\n\n {/* Key Metrics Comparison */}\n

Real-time Performance Comparison

Traditional RL - Progress: {regularRLStats[step].progress}%

\n {renderProgressBar(regularRLStats[step].progress, 100)}\n

\n Time units: {regularRLStats[step].timeUnits}\n States explored: {regularRLStats[step].explored}\n

vers-Enhanced RL - Progress: {versRLStats[step].progress}%

\n {renderProgressBar(versRLStats[step].progress, 100)}\n

\n Time units: {versRLStats[step].timeUnits}\n States explored: {versRLStats[step].explored}\n

\n {step >= 5 && versRLStats[step].optimalFound && !regularRLStats[step].optimalFound && (\n

\n \n vers solved the problem {regularRLStats[7].timeUnits - versRLStats[5].timeUnits} time units faster!\n \n

\n )}\n

\n\n

\n {/* Regular RL */}\n

\n {step >= 6 && (\n

\n Solution found after {regularRLStats[step].timeUnits} time units\n

\n )}\n

Traditional RL Approach

Single sequential path exploration with uniform resource allocation

\n\n

Resource Allocation:

\n {renderResourceBar(\"regular\")}\n

\n\n

\n {renderMaze(\"regular\")}\n

\n\n

Statistics:

\n States explored: {regularRLStats[step].explored}\n
\n Active paths: {regularRLStats[step].paths}\n
\n Time units consumed: {regularRLStats[step].timeUnits}\n
\n Optimal path found:{\" \"}\n {regularRLStats[step].optimalFound ? \"✅\" : \"❌\"}\n

\n\n

\n {step === 0 &&

Starting the exploration from the initial state with a single agent.

}\n {step === 1 && (\n

\n Exploring a single path, trying different actions one at a time. No ability to pursue multiple\n strategies simultaneously.\n

\n )}\n {step === 2 && (\n

Continuing to explore the maze sequentially. Must fully evaluate each path before trying others.

\n )}\n {step === 3 && (\n

Single agent explores multiple paths one after another, which is time-consuming and inefficient.

\n )}\n {step === 4 &&

Significant time spent exploring suboptimal paths before finding better alternatives.

}\n {step === 5 && (\n

Still exploring various corridors methodically but inefficiently, with no ability to prioritize.

\n )}\n {step === 6 && (\n

\n Finally found a path to the goal, but only after extensive exploration of many states.\n

\n )}\n {step === 7 &&

Path optimization complete, but required exploring 102 states over 7 time units.

}\n

\n\n {step >= 5 && (\n

LIMITATIONS:

Cannot explore multiple paths in parallel
Wastes resources on unpromising paths
No dynamic resource allocation
Sequential exploration is time-inefficient

\n )}\n

\n\n {/* vers RL */}\n

\n {step >= 5 && (\n

\n Solution found after only {versRLStats[5].timeUnits} time units!\n

\n )}\n

vers-Enhanced RL

Multi-branch parallel exploration with intelligent resource allocation

\n\n

Dynamic Resource Allocation:

\n {renderResourceBar(\"vers\")}\n

\n\n

\n {renderMaze(\"vers\")}\n

\n\n

Statistics:

\n States explored: {versRLStats[step].explored}\n
\n Total branches created: {versRLStats[step].branches}\n
\n Active branches: {versRLStats[step].active}\n
\n Time units consumed: {versRLStats[step].timeUnits}\n
\n Optimal path found:{\" \"}\n {versRLStats[step].optimalFound ? \"✅\" : \"❌\"}\n

\n\n

\n {step === 0 &&

Starting with a single computational branch, ready to fork when needed.

}\n {step === 1 && (\n

\n Created multiple VM branches (A, B, C) to explore different strategies simultaneously in isolated\n environments.\n

\n )}\n {step === 2 && (\n

\n Branch C showed poor progress and was pruned. Resources reallocated to promising branches. New branches\n D and E created at decision points.\n

\n )}\n {step === 3 && (\n

\n Branches B and E pruned after evaluation. Branch A showing promising results gets 60% of resources.\n Adapting exploration strategy in real-time.\n

\n )}\n {step === 4 && (\n

\n Branch D pruned as metrics showed limited progress. Resources concentrated on branches A (75%) and F\n (25%).\n

\n )}\n {step === 5 && (\n

\n Branch F pruned. Branch G created as alternative path. Branch A reaches goal! Solution found in only 5\n time units!\n

\n )}\n {step === 6 && (\n

\n Optimal path confirmed! All other branches pruned, focusing 100% of resources on the best solution for\n final optimization.\n

\n )}\n {step === 7 && (\n

\n Exploration complete with 42% fewer states explored (59 vs 102) and 29% faster completion (5 vs 7 time\n units).\n

\n )}\n

\n\n {step >= 5 && (\n

ADVANTAGES:

Parallel exploration of multiple strategies
Intelligent pruning of unpromising paths
Dynamic resource allocation to promising branches
29% faster solution discovery
42% more efficient state exploration

\n )}\n

\n\n {showSummary && (\n

Performance Summary: Why vers is Better

\n\n

Speed Comparison

Traditional RL:

\n 7 time units\n

vers-Enhanced:

\n 5 time units (29% faster)\n

\n\n

Exploration Efficiency

Traditional RL:

\n 102 states explored\n

vers-Enhanced:

\n 59 states (42% more efficient)\n

\n\n

Traditional RL Limitations

Sequential exploration wastes time on unproductive paths
No ability to try multiple approaches simultaneously
Uniform resource allocation regardless of path promise
Must fully explore each path before trying alternatives
No mechanisms for early termination of dead-end paths

\n\n

vers Advantages

Parallel exploration of multiple paths simultaneously
Dynamic resource allocation to promising branches
Intelligent pruning of unpromising branches
System-level isolation of different computational approaches
Ability to spawn new branches at decision points
29% faster solution discovery
42% more efficient exploration

\n\n

Real-world Impact

\n In complex real-world RL applications, vers would provide an even more dramatic advantage:\n

Full VM-level isolation for each computational branch
Copy-on-write filesystem efficiency for minimal storage overhead
Ability to test completely different algorithms in parallel
Dynamic resource allocation at the compute infrastructure level
Potential for exponential speedups in complex search spaces

\n For tasks like large-scale optimization, complex resource allocation, or systems with many parameters to\n tune, vers could transform computation by making exploration far more efficient than traditional\n sequential approaches.\n

\n )}\n

\n );\n};\n\nexport default RLVersComparison;","deleted","label",[98],"permalink","https://www.val.town/x/alephnotation/neatTealCobra/code/main.tsx","extension","actionData","errors"]