From f900e5340918cd52b1801d94f8cbacd883e9bda2 Mon Sep 17 00:00:00 2001 From: RicardoRibeiroRodrigues Date: Thu, 23 May 2024 16:42:36 -0300 Subject: [PATCH 01/18] New reward and terminations for both envs --- docs/Documentation/docsCoverage.md | 11 ++++------- docs/Documentation/docsSearch.md | 11 +++-------- 2 files changed, 7 insertions(+), 15 deletions(-) diff --git a/docs/Documentation/docsCoverage.md b/docs/Documentation/docsCoverage.md index 81a218d..87e9fd3 100644 --- a/docs/Documentation/docsCoverage.md +++ b/docs/Documentation/docsCoverage.md @@ -265,19 +265,16 @@ The reward returns a dictionary with the drones names as keys and their respectf The rewards values goes as follows: -- **`Default Action`**: Every action receives a baseline reward of `0`. -- **`Leaving the Grid`**: A penalty of `-10` is applied if a drone leaves the grid boundaries. -- **`Exceeding Time Limit`**: A penalty of `-100` is imposed if the drone does not locate all the cells before the timestep_limit is exceeded. -- **`Collision`**: If drones collide, each involved drone receives a penalty of `-10`. -- **`Searching a Cell`**: The reward for searching a cell is proportional to the probability p of the cell being searched, denoted as `10 + (1 / (timestep)) * prob_matrix * 1_000`. -- **`complete the serching`**: If all cells are searched, the reward is `100`. +- **`Default Action`**: Every action receives a baseline reward of `-0.2`. +- **`Searching a Cell`**: The reward for searching a cell is proportional to the probability p of the cell being searched, denoted as `1 + (1 - Ts / Ts_limit) * p * n_cells`. +- **`Complete the searching`**: If all cells are searched, the reward is `n_cells + n_cells * (1 - Ts / Ts_limit)`. ### Termination & Truncation The termination and truncation variables return a dictionary with all drones as keys and boolean as values. By default, these values are set to `False` and will switch to `True` under any of the following conditions: -- **`Collision`**: If two or more drones collide. - **`Time Limit Exceeded`**: If the simulation's timestep exceeds the `timestep_limit`. +- **`Done searching all cells`**: If the drones have searched all cells with probability > 0. #### For example, the dictionary might look like this: diff --git a/docs/Documentation/docsSearch.md b/docs/Documentation/docsSearch.md index 36a8bbd..93fc6d6 100644 --- a/docs/Documentation/docsSearch.md +++ b/docs/Documentation/docsSearch.md @@ -274,22 +274,17 @@ An output example can be seen below. ### Reward -The reward returns a dictionary with the drones names as keys and their respectful rewards as values. For example `{'drone0': 1, 'drone1': 89.0, 'drone2': 1}` +The reward returns a dictionary with the drones names as keys and their respectful rewards as values. For example `{'drone0': 1.0, 'drone1': 0.0, 'drone2': 1.0}` The rewards values goes as follows: -- **`Default Action`**: Every action receives a baseline reward of `0.1`. -- **`Leaving the Grid`**: A penalty of `-200` is applied if a drone leaves the grid boundaries. -- **`Exceeding Time Limit`**: A penalty of `-200` is imposed if the drone does not locate the person before the timestep_limit is exceeded. -- **`Collision`**: If drones collide, each involved drone receives a penalty of `-200`. -- **`Searching a Cell`**: The reward for searching a cell is proportional to the probability p of the cell being searched, denoted as `[0:p]`. -- **`Finding the Person`**: If a drone successfully locates the person within a cell, the reward is `200 + 200 * ((1 - timestep) / timestep_limit)`, encouraging faster discovery. +- **`Default Action`**: Every action receives a baseline reward of `0.0`. +- **`Finding the Person`**: If a drone successfully locates the person within a cell, the reward is `1 + 1 * ((1 - timestep) / timestep_limit)`, encouraging faster discovery. ### Termination & Truncation The termination and truncation variables return a dictionary with all drones as keys and boolean as values. By default, these values are set to `False` and will switch to `True` under any of the following conditions: -- **`Collision`**: If two or more drones collide. - **`Time Limit Exceeded`**: If the simulation's timestep exceeds the `timestep_limit`. - **`All PIWs Found`**: If all Persons in Water (PIWs) have been successfully located. From 5ee83e2b8d5f42535ced320bc55c7f9cf836b746 Mon Sep 17 00:00:00 2001 From: Pedro2712 Date: Thu, 23 May 2024 17:55:57 -0300 Subject: [PATCH 02/18] =?UTF-8?q?Co-authored-by:=20Ricardo=20Ribeiro=20Rod?= =?UTF-8?q?rigues=20=20C?= =?UTF-8?q?o-authored-by:=20Renato=20Laffranchi=20Falc=C3=A3o=20?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- DSSE/environment/env_base.py | 3 +- docs/.vitepress/config.mts | 13 +- docs/Documentation/docsAlgorithms.md | 46 ++++-- docs/Documentation/docsCoverage.md | 47 ++++-- docs/Documentation/docsSearch.md | 13 +- docs/public/gifs/PPO_coverage.gif | Bin 0 -> 294367 bytes package-lock.json | 205 ++++++++++++++------------- package.json | 2 +- 8 files changed, 195 insertions(+), 134 deletions(-) create mode 100644 docs/public/gifs/PPO_coverage.gif diff --git a/DSSE/environment/env_base.py b/DSSE/environment/env_base.py index 1d2fb5d..85e34a1 100644 --- a/DSSE/environment/env_base.py +++ b/DSSE/environment/env_base.py @@ -21,8 +21,9 @@ def __init__( drone_amount=1, drone_speed=10, probability_of_detection=1, + cell_size=130, ) -> None: - self.cell_size = 130 # in meters + self.cell_size = cell_size # in meters self.grid_size = grid_size self._was_reset = False if not isinstance(drone_amount, int): diff --git a/docs/.vitepress/config.mts b/docs/.vitepress/config.mts index 8253478..ec6a737 100644 --- a/docs/.vitepress/config.mts +++ b/docs/.vitepress/config.mts @@ -51,6 +51,7 @@ export default defineConfig({ { text: 'Reward', link: '/Documentation/docsSearch#reward' }, { text: 'Termination & Truncation', link: '/Documentation/docsSearch#termination-truncation' }, { text: 'Info', link: '/Documentation/docsSearch#info' }, + { text: 'Stay Updated', link: '/Documentation/docsSearch#stay-updated' }, { text: 'How to Cite This Work', link: '/Documentation/docsSearch#how-to-cite-this-work' }, { text: 'License', link: '/Documentation/docsSearch#license' }, ] @@ -68,6 +69,7 @@ export default defineConfig({ { text: 'Reward', link: '/Documentation/docsCoverage#reward' }, { text: 'Termination & Truncation', link: '/Documentation/docsCoverage#termination-truncation' }, { text: 'Info', link: '/Documentation/docsCoverage#info' }, + { text: 'Stay Updated', link: '/Documentation/docsCoverage#stay-updated' }, { text: 'License', link: '/Documentation/docsCoverage#license' }, ] }, @@ -75,7 +77,16 @@ export default defineConfig({ // collapsed: false, // text: 'Algorithms', // items: [ - // { text: 'Documentation Under Construction', link: '/Documentation/docsAlgorithms#build' }, + // { text: 'About', link: '/Documentation/docsAlgorithms#about' }, + // { text: 'Quick Start', link: '/Documentation/docsAlgorithms#quick-start' }, + // { text: 'General Info', link: '/Documentation/docsAlgorithms#general-info' }, + // { text: 'Built in Functions', link: '/Documentation/docsAlgorithms#built-in-functions' }, + // { text: 'Probability Matrix', link: '/Documentation/docsAlgorithms#probability-matrix' }, + // { text: 'Reward', link: '/Documentation/docsAlgorithms#reward' }, + // { text: 'Termination & Truncation', link: '/Documentation/docsAlgorithms#termination-truncation' }, + // { text: 'Info', link: '/Documentation/docsAlgorithms#info' }, + // { text: 'Stay Updated', link: '/Documentation/docsAlgorithms#stay-updated' }, + // { text: 'License', link: '/Documentation/docsAlgorithms#license' }, // ] // } ], diff --git a/docs/Documentation/docsAlgorithms.md b/docs/Documentation/docsAlgorithms.md index 634824a..a26ba20 100644 --- a/docs/Documentation/docsAlgorithms.md +++ b/docs/Documentation/docsAlgorithms.md @@ -1,19 +1,43 @@ -# Algorithms Documentation Coming Soon! +# Drone Swarm Search: Algorithms -![Under Construction](/pics/build.png) +## About -We are currently working hard on developing comprehensive documentation for the algorithms used in the DSSE Training Environment. This documentation will provide detailed insights and guidelines on how to effectively utilize and implement our algorithms. +Welcome to our [Algorithms repository](https://github.com/pfeinsper/drone-swarm-search-algorithms)! These algorithms are specifically tailored for the environments available in `DSSE`, aimed at optimizing drone swarm coordination and search efficiency. -🛠️ **What to Expect?** -- **Detailed Descriptions**: Each algorithm will be thoroughly explained, including its purpose, application, and integration within our environment. -- **Practical Examples**: We will provide examples to illustrate how these algorithms can be applied in real-world scenarios. -- **Technical Specifications**: Expect in-depth technical details to aid developers and researchers in implementing the algorithms. +Explore a diverse range of implementations that leverage the latest advancements in machine learning to solve complex coordination tasks in dynamic and unpredictable environments. Our repository offers state-of-the-art solutions designed to enhance the performance and adaptability of autonomous drone swarms, making them more efficient and effective in various search and rescue missions. -📅 **Estimated Availability** -- The full documentation is expected to be available soon. We are doing our best to ensure that it meets high standards of clarity and usefulness. +

+ +
+ Fig 1: Trained agents in action with PPO algorithm. +

-### Stay Updated +## Algorithms used +All the algorithms developed here were done using the [RLlib library](https://docs.ray.io/en/latest/rllib/index.html) +Both implementations are done trough a Multi-Input Convolutional Neural Network, that receives the probability matrix as one of the inputs, and the agents positions as the other, in other words, the complete observations of the environment. -We appreciate your patience and interest in our work. If you have any questions or need immediate assistance regarding our algorithms, please do not hesitate to contact us via our [GitHub Issues page](https://github.com/pfeinsper/drone-swarm-search-algorithms/issues). +### Proximal Policy Optmization (PPO) +There are two implementations of the Proximal Policy Optimization (PPO) algorithm, a centralized and descentralized versions. The difference being that the centralized approach creates a single neural network for all the agents, while the descentralized version creates a neural network per-agent. +### Deep Q-Network (DQN) +There is a implementation for the Deep Q-Networks algorithm as well, using the same network architecture as the PPO version, changing only parts related to the output, that is specific to each of the algorithms. + + +## How to run + +A variety of algorithm implementations are available in the algorithms directory. To run any script within this directory, use the following command: + +```bash +python SCRIPT.py +``` +exchanging `SCRIPT` with the desired script name. + +Please note that configurations and parameters can be adjusted for both the environment and RLlib. Make sure to review and update these settings as needed to suit your specific requirements. + +## Stay Updated + +We appreciate your patience and interest in our work. If you have any questions or need immediate assistance regarding our `algorithms`, please do not hesitate to contact us via our [GitHub Issues page](https://github.com/pfeinsper/drone-swarm-search-algorithms/issues). + +## License +This documentation is licensed under the terms of the [MIT License](https://opensource.org/licenses/MIT). See the LICENSE file for more details. diff --git a/docs/Documentation/docsCoverage.md b/docs/Documentation/docsCoverage.md index 87e9fd3..c3e8d9c 100644 --- a/docs/Documentation/docsCoverage.md +++ b/docs/Documentation/docsCoverage.md @@ -91,18 +91,23 @@ We incorporated 8 actions in this environment to enable the use of agents traine ::: ### Inputs -| Inputs | Possible Values | Default Values | -| ------------- | ------------- | ------------- | -| `render_mode` | `"ansi" or "human"` | `"ansi"` | -| `render_grid` | `bool` | `True` | -| `render_gradient` | `bool` | `True` | -| `timestep_limit` | `int` | `100` | -| `disaster_position` | `(float, float)` | `(-24.04, -46.17)` | -| `drone_amount` | `int` | `1` | -| `drone_speed` | `int` | `10` | -| `drone_probability_of_detection`| `float` | `1.0` | -| `pre_render_time` | `int` | `10` | -| `prob_matrix_path` | `string` | `None` | +| Inputs | Possible Values | Default Values | +| ------------- | ------------- | ------------- | +| `render_mode` | `"ansi" or "human"` | `"ansi"` | +| `render_grid` | `bool` | `True` | +| `render_gradient` | `bool` | `True` | +| `timestep_limit` | `int` | `100` | +| `disaster_position` | `(float, float)` | `(-24.04, -46.17)` | +| `drone_amount` | `int` | `1` | +| `drone_speed` | `int` | `10` | +| `drone_probability_of_detection` | `float` | `1.0` | +| `pre_render_time` | `int` | `10` | +| `prob_matrix_path` | `string` | `None` | +| `particle_amount` | `int` | `50,000` | +| `particle_radius` | `int` | `800` | +| `num_particle_to_filter_as_noise` | `int` | `1` | +| `start_time` | `datetime` | `None` | +| `grid_cell_size` | `int` | `130` | - **`render_mode`**: @@ -127,6 +132,16 @@ We incorporated 8 actions in this environment to enable the use of agents traine - **`prob_matrix_path`**: This **string** parameter allows the user to specify the path to file of a already simulated probability matrix. The file should be a `.npy` file containing a probability matrix. If this parameter is not specified, the environment will generate a new probability matrix. +- **`particle_amount`**: This **int** parameter allows the user to customize the number of particles used in the `Lagrangian particle model` used to create the probability matrix. + +- **`particle_radius`**: This **int** parameter allows the user to `customize` the radius that the particles are randomly placed at the start of the Lagrangian particle model simulation. + +- **`num_particle_to_filter_as_noise`**: This **int** parameter allows the user to modify the number of particles in each cell that are filtered to zero. (e.g. if the number of value is `1`, cells with only 1 particle in the end of simulation will be filtered to 0). + +- **`start_time`**: This **datetime** parameter allows the user to specify the `start time` of the simulation. If not specified, the simulation will start at the current time. + +- **`grid_cell_size`**: This **int** parameter allows the user to specify the `size of the grid` cells in meters. The default value is 130 meters. + ## Built in Functions ### `env.reset`: @@ -266,7 +281,7 @@ The reward returns a dictionary with the drones names as keys and their respectf The rewards values goes as follows: - **`Default Action`**: Every action receives a baseline reward of `-0.2`. -- **`Searching a Cell`**: The reward for searching a cell is proportional to the probability p of the cell being searched, denoted as `1 + (1 - Ts / Ts_limit) * p * n_cells`. +- **`Searching a Cell`**: The reward for searching a cell is proportional to the probability p of the cell being searched, denoted as `1 + (1 - Ts / Ts_limit) * p * n_cells`, being `n_cells` the number of cells with probability greater than 0, Ts being the terminal timestep and Ts_limit the limit of timesteps. - **`Complete the searching`**: If all cells are searched, the reward is `n_cells + n_cells * (1 - Ts / Ts_limit)`. ### Termination & Truncation @@ -274,7 +289,7 @@ The rewards values goes as follows: The termination and truncation variables return a dictionary with all drones as keys and boolean as values. By default, these values are set to `False` and will switch to `True` under any of the following conditions: - **`Time Limit Exceeded`**: If the simulation's timestep exceeds the `timestep_limit`. -- **`Done searching all cells`**: If the drones have searched all cells with probability > 0. +- **`Done searching all cells`**: If the agents have searched all cells with probability > 0. #### For example, the dictionary might look like this: @@ -347,5 +362,9 @@ The `env.save_matrix()` method is not only convenient for saving the probability `env.close()` will simply close the render window. Not a necessary function but may be used. +## Stay Updated + +We appreciate your patience and interest in our work. If you have any questions or need immediate assistance regarding our `Coverage Environment`, please do not hesitate to contact us via our [GitHub Issues page](https://github.com/pfeinsper/drone-swarm-search/issues). + ## License This documentation is licensed under the terms of the [MIT License](https://opensource.org/licenses/MIT). See the LICENSE file for more details. diff --git a/docs/Documentation/docsSearch.md b/docs/Documentation/docsSearch.md index 93fc6d6..5140bde 100644 --- a/docs/Documentation/docsSearch.md +++ b/docs/Documentation/docsSearch.md @@ -74,7 +74,6 @@ while not done: ## General Info - | Import | `from DSSE import DroneSwarmSearch` | | ------------------ | -------------------------------------------------- | | Action Space | Discrete (9) | @@ -83,7 +82,6 @@ while not done: | Observation Space | `{droneN: ((x, y), probability_matrix)}` | ### Action Space - | Value | Meaning | | ----- | ---------------------- | | 0 | Move Left | @@ -100,6 +98,7 @@ while not done: | Inputs | Possible Values | Default Values | | ------------- | ------------- | ------------- | | `grid_size` | `int(N)` | `20` | +| `cell_size` | `int(N)` | `130` | | `render_mode` | `"ansi" or "human"` | `"ansi"` | | `render_grid` | `bool` | `False` | | `render_gradient` | `bool` | `True` | @@ -114,7 +113,9 @@ while not done: | `probability_of_detection`| `float` | `1.0` | | `pre_render_time` | `int` | `0` | -- **`grid_size`**: Defines the area in which the search will happen. It should always be an integer greater than one. +- **`grid_size`**: Integer value that defines the area in which the search will happen. It should always be an integer greater than one. + +- **`cell_size`**: Integer value that defines the size of each cell, in meters.. - **`render_mode`**: @@ -279,7 +280,7 @@ The reward returns a dictionary with the drones names as keys and their respectf The rewards values goes as follows: - **`Default Action`**: Every action receives a baseline reward of `0.0`. -- **`Finding the Person`**: If a drone successfully locates the person within a cell, the reward is `1 + 1 * ((1 - timestep) / timestep_limit)`, encouraging faster discovery. +- **`Finding the Person`**: If a drone successfully locates the person within a cell, the reward is `1 + 1 * (1 - timestep / timestep_limit)`, encouraging faster discovery. ### Termination & Truncation @@ -340,5 +341,9 @@ If you use this package, please consider citing it with this piece of BibTeX: } ``` +## Stay Updated + +We appreciate your patience and interest in our work. If you have any questions or need immediate assistance regarding our `Search Environment`, please do not hesitate to contact us via our [GitHub Issues page](https://github.com/pfeinsper/drone-swarm-search/issues). + ## License This documentation is licensed under the terms of the [MIT License](https://opensource.org/licenses/MIT). 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"vue": "^3.4.25" + "shiki": "^1.5.2", + "vite": "^5.2.11", + "vue": "^3.4.27" }, "bin": { "vitepress": "bin/vitepress.js" @@ -2421,16 +2422,16 @@ } }, "node_modules/vue": { - "version": "3.4.25", - "resolved": "https://registry.npmjs.org/vue/-/vue-3.4.25.tgz", - "integrity": "sha512-HWyDqoBHMgav/OKiYA2ZQg+kjfMgLt/T0vg4cbIF7JbXAjDexRf5JRg+PWAfrAkSmTd2I8aPSXtooBFWHB98cg==", + "version": "3.4.27", + "resolved": "https://registry.npmjs.org/vue/-/vue-3.4.27.tgz", + "integrity": "sha512-8s/56uK6r01r1icG/aEOHqyMVxd1bkYcSe9j8HcKtr/xTOFWvnzIVTehNW+5Yt89f+DLBe4A569pnZLS5HzAMA==", "dev": true, "dependencies": { - "@vue/compiler-dom": "3.4.25", - "@vue/compiler-sfc": "3.4.25", - "@vue/runtime-dom": "3.4.25", - "@vue/server-renderer": "3.4.25", - "@vue/shared": "3.4.25" + "@vue/compiler-dom": "3.4.27", + "@vue/compiler-sfc": "3.4.27", + "@vue/runtime-dom": "3.4.27", + "@vue/server-renderer": "3.4.27", + "@vue/shared": "3.4.27" }, "peerDependencies": { "typescript": "*" diff --git a/package.json b/package.json index 7adced6..9a9357d 100644 --- a/package.json +++ b/package.json @@ -8,6 +8,6 @@ "@types/node": "^20.12.5", "gh-pages": "^6.1.1", "markdown-it-mathjax3": "^4.3.2", - "vitepress": "^1.1.4" + "vitepress": "^1.2.2" } } From ed2034e9e582910c9f67c024866824eb521e9366 Mon Sep 17 00:00:00 2001 From: Pedro2712 Date: Thu, 23 May 2024 17:59:43 -0300 Subject: [PATCH 03/18] Resolvendo conflito --- DSSE/environment/env_base.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/DSSE/environment/env_base.py b/DSSE/environment/env_base.py index 85e34a1..1d2fb5d 100644 --- a/DSSE/environment/env_base.py +++ b/DSSE/environment/env_base.py @@ -21,9 +21,8 @@ def __init__( drone_amount=1, drone_speed=10, probability_of_detection=1, - cell_size=130, ) -> None: - self.cell_size = cell_size # in meters + self.cell_size = 130 # in meters self.grid_size = grid_size self._was_reset = False if not isinstance(drone_amount, int): From bebf3612ba47800dc7f44f58e396b557344b8354 Mon 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