Displaying 1 to 5 from 5 results

ElegantRL - Lightweight, efficient and stable implementations of deep reinforcement learning algorithms using PyTorch

  •    Python

ElegantRL is featured with lightweight, efficient and stable, for researchers and practitioners. Lightweight: The core codes <1,000 lines (check elegantrl/tutorial), using PyTorch (train), OpenAI Gym (env), NumPy, Matplotlib (plot).

HinetPy - A Python package to request and process seismic waveform data from Hi-net.

  •    Python

HinetPy is a Python package aiming to automate and simplify tedious data request, downloading and format conversion tasks related to NIED Hi-net. The power of HinetPy makes it simple to request continuous waveform data from Hi-net, convert the data into SAC format and extract instrumental responses as SAC polezero files.

jaxrl - Jax (Flax) implementation of algorithms for Deep Reinforcement Learning with continuous action spaces

  •    Jupyter

The goal of this repository is to provide simple and clean implementations to build research on top of. Please do not use this repository for baseline results and use the original implementations instead (SAC, AWAC, DrQ). If you want to run this code on GPU, please follow instructions from the official repository.




Deep-Reinforcement-Learning-With-Python - Master classic RL, deep RL, distributional RL, inverse RL, and more using OpenAI Gym and TensorFlow with extensive Math

  •    Jupyter

With significant enhancement in the quality and quantity of algorithms in recent years, this second edition of Hands-On Reinforcement Learning with Python has been completely revamped into an example-rich guide to learning state-of-the-art reinforcement learning (RL) and deep RL algorithms with TensorFlow and the OpenAI Gym toolkit. In addition to exploring RL basics and foundational concepts such as the Bellman equation, Markov decision processes, and dynamic programming, this second edition dives deep into the full spectrum of value-based, policy-based, and actor- critic RL methods with detailed math. It explores state-of-the-art algorithms such as DQN, TRPO, PPO and ACKTR, DDPG, TD3, and SAC in depth, demystifying the underlying math and demonstrating implementations through simple code examples.






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