GitPlanet

Cheap and reliable Node.js hosting starts at $3/month, and $1/month static HTML hosting

Created with love in Canada, visit hostnodejs.com today

Feel like to post an Ad? Learn Details
All Projects → lucas-emery → rocket-league-gym

lucas-emery / rocket-league-gym

Licence: Apache-2.0 license
A Gym-like environment for Reinforcement Learning in Rocket League

Programming Languages

python
139335 projects - #7 most used programming language
Batchfile
5799 projects

Labels

reinforcement-learningrocket-leaguehacktoberfestgym-environment

Projects that are alternatives of or similar to rocket-league-gym

obstacle-env
An environment for an obstacle avoidance task
Stars: ✭ 30 (-71.96%)
Mutual labels:  gym-environment
RocketLeagueReplayParser
No description or website provided.
Stars: ✭ 81 (-24.3%)
Mutual labels:  rocket-league
gym-cryptotrading
OpenAI Gym Environment API based Bitcoin trading environment
Stars: ✭ 111 (+3.74%)
Mutual labels:  gym-environment
trading gym
a unified environment for supervised learning and reinforcement learning in the context of quantitative trading
Stars: ✭ 36 (-66.36%)
Mutual labels:  gym-environment
rocket-league-replay-analysis
UNMAINTAINED - Creating videos for Analyzing Rocket League Replays (https://gitlab.com/enzanki_ars/rocket-league-replay-analysis)
Stars: ✭ 24 (-77.57%)
Mutual labels:  rocket-league
rrrocket
Rocket League Replay parser to JSON -- CLI app
Stars: ✭ 36 (-66.36%)
Mutual labels:  rocket-league
carball
📈 A Rocket League replay decompiling and analysis library
Stars: ✭ 129 (+20.56%)
Mutual labels:  rocket-league
gym-mtsim
A general-purpose, flexible, and easy-to-use simulator alongside an OpenAI Gym trading environment for MetaTrader 5 trading platform (Approved by OpenAI Gym)
Stars: ✭ 196 (+83.18%)
Mutual labels:  gym-environment
gym example
An example implementation of an OpenAI Gym environment used for a Ray RLlib tutorial
Stars: ✭ 25 (-76.64%)
Mutual labels:  gym-environment
rlbot-rust
RLBot for Rust
Stars: ✭ 17 (-84.11%)
Mutual labels:  rocket-league
Neural-Fictitous-Self-Play
Scalable Implementation of Neural Fictitous Self-Play
Stars: ✭ 52 (-51.4%)
Mutual labels:  gym-environment
2048-Gym
This projects aims to use reinforcement learning algorithms to play the game 2048.
Stars: ✭ 68 (-36.45%)
Mutual labels:  gym-environment
stadium
A graphical interface for reinforcement learning and gym-based environments. Integrates tensorboard and various configuration utilities for ease of usage.
Stars: ✭ 26 (-75.7%)
Mutual labels:  gym-environment
MuJoCo RL UR5
A MuJoCo/Gym environment for robot control using Reinforcement Learning. The task of agents in this environment is pixel-wise prediction of grasp success chances.
Stars: ✭ 108 (+0.93%)
Mutual labels:  gym-environment
mgym
A collection of multi-agent reinforcement learning OpenAI gym environments
Stars: ✭ 41 (-61.68%)
Mutual labels:  gym-environment
gym-microrts-paper-sb3
RL agent to play μRTS with Stable-Baselines3 and PyTorch
Stars: ✭ 21 (-80.37%)
Mutual labels:  gym-environment

The Rocket League Gym

This is a python API that can be used to treat the game Rocket League as though it were an OpenAI Gym-style environment for Reinforcement Learning projects. This API must be used with the accompanying Bakkesmod plugin.

Requirements

  • A Windows 10 PC
  • Rocket League (Both Steam and Epic are supported)
  • Bakkesmod
  • The RLGym plugin for Bakkesmod (It's installed automatically by pip)
  • Python between versions 3.7 and 3.9 (3.10 not supported).

Installation

Install the library via pip:

pip install rlgym

Once the API is installed, you will need to enable the RLGym plugin from inside the Bakkesmod plugin manager. To do this, first launch the game, then press F2 to open the Bakkesmod menu. Navigate to the plugins tab and open the Plugin Manager. From there, scroll down until you find the RLGym plugin, and enable it. Close the game when this is done.

RLGym is now installed! simply run example.py from our repo to ensure everything works.

Usage

For tutorials and documentation, please visit our Wiki.

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].