{"id":16253760,"url":"https://github.com/selfint/neat_openaigym","last_synced_at":"2025-04-08T12:46:51.123Z","repository":{"id":128267620,"uuid":"207159490","full_name":"selfint/NEAT_OPENAIGYM","owner":"selfint","description":"Using NEAT to solve OpenAI gym environments","archived":false,"fork":false,"pushed_at":"2019-09-16T17:33:22.000Z","size":36,"stargazers_count":2,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-02-14T08:49:12.327Z","etag":null,"topics":["neat-python","neuroevolution","openai-gym","python3"],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/selfint.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2019-09-08T19:02:03.000Z","updated_at":"2024-06-04T23:23:33.000Z","dependencies_parsed_at":null,"dependency_job_id":"e98c25fa-71d2-4668-8bbe-701d8862d1c9","html_url":"https://github.com/selfint/NEAT_OPENAIGYM","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/selfint%2FNEAT_OPENAIGYM","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/selfint%2FNEAT_OPENAIGYM/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/selfint%2FNEAT_OPENAIGYM/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/selfint%2FNEAT_OPENAIGYM/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/selfint","download_url":"https://codeload.github.com/selfint/NEAT_OPENAIGYM/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247846680,"owners_count":21006088,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["neat-python","neuroevolution","openai-gym","python3"],"created_at":"2024-10-10T15:18:29.989Z","updated_at":"2025-04-08T12:46:51.094Z","avatar_url":"https://github.com/selfint.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"\n# Objective\n\nImplement the NEAT algorithm as elegantly as possible, and use it on\nOpenAI-gym environments.\nBased on this paper: \u003chttp://nn.cs.utexas.edu/downloads/papers/stanley.ec02.pdf\u003e.\n\n# Algorithm Outline\n\n## Testing the agents\n\n1. Each agent is represented as a genome, which contains the nodes and connections\nof that genome.\n\n2. The simulation will generate an agent using each genome in its population list, let\nit run in the environment (in turn) and return its accumulated reward.\n\n## Generating a new generation\n\n1. Adjust each genomes fitness based on its species (formula 2 page 13).\n\n2. Generate new genomes using the rules specified in pages 11 - 13.\n\n3. Split the genomes into species based on their 'genetic distance' (formula 1 page 13).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fselfint%2Fneat_openaigym","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fselfint%2Fneat_openaigym","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fselfint%2Fneat_openaigym/lists"}