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 → erfanMhi → Deep-Reinforcement-Learning-CS285-Pytorch

erfanMhi / Deep-Reinforcement-Learning-CS285-Pytorch

Licence: MIT license
Solutions of assignments of Deep Reinforcement Learning course presented by the University of California, Berkeley (CS285) in Pytorch framework

Programming Languages

python
139335 projects - #7 most used programming language
Jupyter Notebook
11667 projects
c
50402 projects - #5 most used programming language
C++
36643 projects - #6 most used programming language

Labels

reinforcement-learningdeep-learningberkeleydeep-reinforcement-learningopenai-gympytorchneural-networkspolicy-gradientdeep-q-learningmujocomodel-based-rlactor-critic-algorithmmodel-free-rl

Projects that are alternatives of or similar to Deep-Reinforcement-Learning-CS285-Pytorch

Pytorch Rl
This repository contains model-free deep reinforcement learning algorithms implemented in Pytorch
Stars: ✭ 394 (+278.85%)
Mutual labels:  deep-reinforcement-learning, openai-gym, policy-gradient, mujoco
Deep-Reinforcement-Learning-With-Python
Master classic RL, deep RL, distributional RL, inverse RL, and more using OpenAI Gym and TensorFlow with extensive Math
Stars: ✭ 222 (+113.46%)
Mutual labels:  deep-reinforcement-learning, openai-gym, policy-gradient
yarll
Combining deep learning and reinforcement learning.
Stars: ✭ 84 (-19.23%)
Mutual labels:  deep-reinforcement-learning, openai-gym, policy-gradient
deep rl acrobot
TensorFlow A2C to solve Acrobot, with synchronized parallel environments
Stars: ✭ 32 (-69.23%)
Mutual labels:  deep-reinforcement-learning, openai-gym, policy-gradient
rl implementations
No description or website provided.
Stars: ✭ 40 (-61.54%)
Mutual labels:  deep-reinforcement-learning, policy-gradient, deep-q-learning
Btgym
Scalable, event-driven, deep-learning-friendly backtesting library
Stars: ✭ 765 (+635.58%)
Mutual labels:  deep-reinforcement-learning, openai-gym, policy-gradient
ddrl
Deep Developmental Reinforcement Learning
Stars: ✭ 27 (-74.04%)
Mutual labels:  deep-reinforcement-learning, openai-gym, mujoco
Lagom
lagom: A PyTorch infrastructure for rapid prototyping of reinforcement learning algorithms.
Stars: ✭ 364 (+250%)
Mutual labels:  deep-reinforcement-learning, policy-gradient, mujoco
Mushroom Rl
Python library for Reinforcement Learning.
Stars: ✭ 442 (+325%)
Mutual labels:  deep-reinforcement-learning, openai-gym, mujoco
Hands On Reinforcement Learning With Python
Master Reinforcement and Deep Reinforcement Learning using OpenAI Gym and TensorFlow
Stars: ✭ 640 (+515.38%)
Mutual labels:  deep-reinforcement-learning, openai-gym, policy-gradient
Deeprl algorithms
DeepRL algorithms implementation easy for understanding and reading with Pytorch and Tensorflow 2(DQN, REINFORCE, VPG, A2C, TRPO, PPO, DDPG, TD3, SAC)
Stars: ✭ 97 (-6.73%)
Mutual labels:  deep-reinforcement-learning, policy-gradient, mujoco
Explorer
Explorer is a PyTorch reinforcement learning framework for exploring new ideas.
Stars: ✭ 54 (-48.08%)
Mutual labels:  deep-reinforcement-learning, policy-gradient
SharkStock
Automate swing trading using deep reinforcement learning. The deep deterministic policy gradient-based neural network model trains to choose an action to sell, buy, or hold the stocks to maximize the gain in asset value. The paper also acknowledges the need for a system that predicts the trend in stock value to work along with the reinforcement …
Stars: ✭ 63 (-39.42%)
Mutual labels:  deep-reinforcement-learning, policy-gradient
omd
JAX code for the paper "Control-Oriented Model-Based Reinforcement Learning with Implicit Differentiation"
Stars: ✭ 43 (-58.65%)
Mutual labels:  deep-reinforcement-learning, model-based-rl
jax-rl
JAX implementations of core Deep RL algorithms
Stars: ✭ 61 (-41.35%)
Mutual labels:  deep-reinforcement-learning, mujoco
Reinforcement Learning
Minimal and Clean Reinforcement Learning Examples
Stars: ✭ 2,863 (+2652.88%)
Mutual labels:  deep-reinforcement-learning, policy-gradient
Pytorch A2c Ppo Acktr Gail
PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR) and Generative Adversarial Imitation Learning (GAIL).
Stars: ✭ 2,632 (+2430.77%)
Mutual labels:  deep-reinforcement-learning, mujoco
a3c-super-mario-pytorch
Reinforcement Learning for Super Mario Bros using A3C on GPU
Stars: ✭ 35 (-66.35%)
Mutual labels:  deep-reinforcement-learning, openai-gym
Fruit-API
A Universal Deep Reinforcement Learning Framework
Stars: ✭ 61 (-41.35%)
Mutual labels:  deep-reinforcement-learning, actor-critic-algorithm
drl grasping
Deep Reinforcement Learning for Robotic Grasping from Octrees
Stars: ✭ 160 (+53.85%)
Mutual labels:  deep-reinforcement-learning, openai-gym
View All Similar Projects ➔

MIT License LinkedIn


CS285

Pytorch Version of homework assignments of Deep Reinforcement Learning Course
Presented by Dr. Sergey Levin at University of California, Berkeley

Report Bug

Table of Contents

About The Project

In this project, we aim to create a Pytorch version of CS285 course whose Tensorflow 1 version is already available at here.

Main Goals

  1. Converting all the Tensorflow 1 code to the newest version of Pytorch
  2. The current version Mujoco environment that has been used in this project is old which requires only using Python < 3.6 version. Therefore, we seek to make this project compatible with the newer version of this library and, consequently, Python >= 3.6.

Completed So Far

  1. Homework 1, 2, 3, and 4 Tensorflow codes have been fully replaced by Pytorch.

Getting Started

Currently, this project is under development, and the same libraries that have been employed in the Tensorflow version of these assignments plus Pytorch are required for running the assignments of this project. However, we are eager to use the versions of these libraries that are presented in the prerequisites section for the future release of this project.

Prerequisites

The libraries that we want to use in the future are as follows.

  • Python >= 3.6
  • Gym >= 0.17
  • Mujoco-py >= 2.0
  • Pytorch >= 1.5.1
  • TensorboardX
  • Matplotlib
  • Ipython
  • Moviepy
  • OpenCV
  • Box2d-py

Usage

The instructions for execution of all of these assignments are given in the Readme documents that are located in each of the homework directories.

Roadmap

See the open issues for a list of known issues.

Contributing

Unfortunately, the current version of this repository is not compatible with the latest versions of libraries, such as Tensorflow and Mojocu-py. As a result, installing the proper versions of these libraries, which can enable you to contribute to this repo, could be a hard challenge. However, since I have been faced with this problem before, I designed a certain number of steps that you can take to install the right versions of these libraries.

  1. Create a new Conda environment based on Python 3.5 and install matplotlib, ipython, and pytorch. Then, activate it.
conda create -n cs285_env python=3.5 matplotlib ipython pytorch=1.5.0
source activate cs285_env
  1. Clone this repository
  2. Install mujoco-py
    1. Get mujoco license key file from its website
    2. Create a .mujoco folder in the home directory and copy the given mjpro150 directory and your license key into it
    mkdir ~/.mujoco/
cd <location_of_your_license_key>
cp mjkey.txt ~/.mujoco/
cd <this_repo>/mujoco
cp -r mjpro150 ~/.mujoco/
    1. Add the following line to bottom of your .bashrc file:
    export LD_LIBRARY_PATH=~/.mujoco/mjpro150/bin/
    1. Build and install mujoco-py 1.50.1.1. It can be downloaded from this link.
    tar -xzf mujoco-py-1.50.1.1.tar.gz 
cd mujoco-py-1.50.1.1
python setup.py install
  3. Install rest of the libraries given in contribution_requirements.txt file using pip
pip install --user --requirement contribution_requirements.txt
  1. At last, it should be considered that before executing scripts of each homework folder (e.g., hw1), you should allow your code to be able to see 'cs285' by executing the following lines:
cd <path_to_hw>
pip install -e .

License

Distributed under the MIT License. See LICENSE file for more information.

Contact

Erfan Miahi - @erfan_mhi - [email protected]

Project Link: https://github.com/erfanMhi/Deep-Reinforcement-Learning-CS285-Pytorch

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].