Displaying 1 to 9 from 9 results

coach - Reinforcement Learning Coach by Intel® AI Lab enables easy experimentation with state of the art Reinforcement Learning algorithms

  •    Python

Coach is a python reinforcement learning research framework containing implementation of many state-of-the-art algorithms. It exposes a set of easy-to-use APIs for experimenting with new RL algorithms, and allows simple integration of new environments to solve. Basic RL components (algorithms, environments, neural network architectures, exploration policies, ...) are well decoupled, so that extending and reusing existing components is fairly painless.

carla - Open-source simulator for autonomous driving research.

  •    C++

CARLA is an open-source simulator for autonomous driving research. CARLA has been developed from the ground up to support development, training, and validation of autonomous urban driving systems. In addition to open-source code and protocols, CARLA provides open digital assets (urban layouts, buildings, vehicles) that were created for this purpose and can be used freely. The simulation platform supports flexible specification of sensor suites and environmental conditions. If you want to benchmark your model in the same conditions as in our CoRL’17 paper, check out Benchmarking.

babyai - BabyAI platform

  •    Python

A platform for simulating language learning with a human in the loop. This is a on-going research project based at Mila. Start by manually installing PyTorch. See the PyTorch website for installation instructions specific to your platform.

pantheon - Pantheon of Congestion Control

  •    Python

The Pantheon contains wrappers for many popular practical and research congestion control schemes. The Pantheon enables them to run on a common interface, and has tools to benchmark and compare their performances. Pantheon tests can be run locally over emulated links using mahimahi or over the Internet to a remote machine. Our website is https://pantheon.stanford.edu, where you can find more information about Pantheon, including supported schemes, measurement results on a global testbed so far, and our paper at USENIX ATC 2018 (Awarded Best Paper).

gail-tf - Tensorflow implementation of generative adversarial imitation learning

  •    Python

The trained model will save in ./checkpoint, and its precise name will vary based on your optimization method and environment ID. Choose the last checkpoint in the series. Note: The following hyper-parameter setting is the best that I've tested (simple grid search on setting with 1500 trajectories), just for your information.

Imitation-Learning-Dagger-Torcs - A Simple Example for Imitation Learning with Dataset Aggregation (DAGGER) on Torcs Env

  •    Python

This repository implements a simple algorithm for imitation learning: DAGGER. In this example, the agent only learns to control the steer [-1, 1], the speed is computed automatically in gym_torcs.TorcsEnv. It will start a Torcs server at the beginning of every episode, and terminate the server when the car crashs or the speed is too low. Note that, the self-contained gym_torcs.py is modified from Gym-Torcs, you can try different settings (like default speed, terminated speed) by modifying it.

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