falken
Falken provides developers with a service that allows them to train AI that can play their games (by google-research)
f-IRL
Inverse Reinforcement Learning via State Marginal Matching, CoRL 2020 (by twni2016)
| falken | f-IRL | |
|---|---|---|
| 2 | 2 | |
| 253 | 35 | |
| 0.0% | - | |
| 0.0 | 1.8 | |
| about 1 month ago | 10 months ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
falken
Posts with mentions or reviews of falken.
We have used some of these posts to build our list of alternatives
and similar projects.
- How long until AI can play a game like Red Dead Redemption 2?
-
Google AI Introduces A Machine Learning Based System For Game Developers To Quickly And Efficiently Train Game-Testing Agents
Google AI recently announced a machine learning-based framework that game developers could use to train game-testing agents quickly and efficiently, freeing human testers to focus on more complicated problems. The resulting system requires no machine learning (ML) expertise, works with a wide range of popular game genres, and can train an ML policy, which generates game actions from the game state on a single game instance in less than an hour. Google AI has also provided an open-source library that shows how these techniques may be used in practice.
f-IRL
Posts with mentions or reviews of f-IRL.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-09-12.
-
Can you use the reward learned in generative adversarial imitation learning in order to train from scratch?
Code for https://arxiv.org/abs/2011.04709 found: https://github.com/twni2016/f-IRL
-
How to pretrain a model on expert data?
Try using an imitation learning algorithm. Two popular options are MaxEnt IRL and GAIL. This repository has GAIL implementation and this repository has MaxEnt IRL and GAIL implementation. There are other implementations too that you can check out.
What are some alternatives?
When comparing falken and f-IRL you can also consider the following projects:
yolov3 - YOLOv3 in PyTorch > ONNX > CoreML > TFLite
Meta-SAC - Auto-tune the Entropy Temperature of Soft Actor-Critic via Metagradient - 7th ICML AutoML workshop 2020
imitation - Clean PyTorch implementations of imitation and reward learning algorithms
ElegantRL - Massively Parallel Deep Reinforcement Learning. 🔥
eirli - An Empirical Investigation of Representation Learning for Imitation (EIRLI), NeurIPS'21
tianshou - An elegant PyTorch deep reinforcement learning library.