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Pull request overview
Adds a new Grid World reinforcement learning visualization to the AI tab in math_explorer_gui, integrating a simple deterministic grid environment with a tabular Q-learning agent and UI rendering.
Changes:
- Added a new
GridWorldTool(environment + Q-learning stepping/training + grid rendering) undermath_explorer_gui/src/tabs/ai/. - Registered the new tool in the AI tab module/tool list.
- Minor formatting-only adjustments in existing GUI code and marked the Grid World roadmap item complete.
Reviewed changes
Copilot reviewed 5 out of 5 changed files in this pull request and generated 6 comments.
Show a summary per file
| File | Description |
|---|---|
| todo_gui.md | Marks the Grid World visualization roadmap item as completed. |
| math_explorer_gui/src/tabs/battery_degradation/lifetime_estimator.rs | Import formatting cleanup. |
| math_explorer_gui/src/tabs/ai/mod.rs | Exposes grid_world module and registers GridWorldTool in AiTab. |
| math_explorer_gui/src/tabs/ai/grid_world.rs | New Grid World RL visualization tool + MDP environment + Q-learning stepping/training UI. |
| math_explorer_gui/src/tabs/ai/attention_maps.rs | Formatting-only cleanup (line wrapping). |
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| if ui.button("Train (100 Episodes)").clicked() { | ||
| for _ in 0..100 { | ||
| let mut temp_steps = 0; | ||
| while !self.env.is_terminal(&self.current_state) && temp_steps < 100 { | ||
| self.step_agent(); | ||
| temp_steps += 1; | ||
| } | ||
| self.reset_episode(); | ||
| } | ||
| } |
| } | ||
| if ui.button("Reset Agent").clicked() { | ||
| self.agent = TabularQAgent::new(0.1, 0.9, 0.1); | ||
| self.reset_episode(); |
| use math_explorer::ai::reinforcement_learning::{ | ||
| algorithms::TabularQAgent, Action, MarkovDecisionProcess, State, | ||
| }; | ||
| use std::hash::Hash; |
| let agent = TabularQAgent::new(0.1, 0.9, 0.1); | ||
| Self { | ||
| current_state: env.start, | ||
| env, | ||
| agent, | ||
| episodes: 0, | ||
| total_reward: 0.0, | ||
| steps: 0, | ||
| } | ||
| } | ||
| } | ||
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| impl GridWorldTool { | ||
| fn step_agent(&mut self) { | ||
| if self.env.is_terminal(&self.current_state) { | ||
| self.reset_episode(); | ||
| return; | ||
| } | ||
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| let actions = self.env.actions(&self.current_state); | ||
| if actions.is_empty() { | ||
| return; | ||
| } | ||
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| if let Some(action) = self.agent.select_action(&self.current_state, &actions) { | ||
| let mut expected_next = self.current_state; |
| let mut expected_next = self.current_state; | ||
| match action { | ||
| Move::Up => expected_next.y -= 1, | ||
| Move::Down => expected_next.y += 1, | ||
| Move::Left => expected_next.x -= 1, | ||
| Move::Right => expected_next.x += 1, | ||
| } | ||
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| let is_valid = expected_next.x >= 0 | ||
| && expected_next.x < self.env.width | ||
| && expected_next.y >= 0 | ||
| && expected_next.y < self.env.height; | ||
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| let next_state = if is_valid { | ||
| expected_next | ||
| } else { | ||
| self.current_state | ||
| }; | ||
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| traps: vec![GridState { x: 2, y: 2 }, GridState { x: 3, y: 2 }], | ||
| gamma: 0.9, | ||
| }; | ||
| let agent = TabularQAgent::new(0.1, 0.9, 0.1); |
Implemented Grid World visualization tool for the Reinforcement Learning module under Artificial Intelligence. Integrates
TabularQAgentwith UI rendering for deterministic Q-Learning execution on a simple grid layout with a goal and traps.grid_world.rstotabs/ai/GridWorldToolinAiTabviaai/mod.rsMarkovDecisionProcesstraitsPR created automatically by Jules for task 2286339306623554352 started by @fderuiter