kengz / Openai_lab
Licence: mit
An experimentation framework for Reinforcement Learning using OpenAI Gym, Tensorflow, and Keras.
Stars: ✭ 313
Programming Languages
python
139335 projects - #7 most used programming language
Labels
Projects that are alternatives of or similar to Openai lab
Reinforcement learning tutorial with demo
Reinforcement Learning Tutorial with Demo: DP (Policy and Value Iteration), Monte Carlo, TD Learning (SARSA, QLearning), Function Approximation, Policy Gradient, DQN, Imitation, Meta Learning, Papers, Courses, etc..
Stars: ✭ 442 (+41.21%)
Mutual labels: reinforcement-learning, deep-reinforcement-learning, policy-gradient, actor-critic
Pytorch Rl
This repository contains model-free deep reinforcement learning algorithms implemented in Pytorch
Stars: ✭ 394 (+25.88%)
Mutual labels: reinforcement-learning, deep-reinforcement-learning, policy-gradient, ddpg
rl implementations
No description or website provided.
Stars: ✭ 40 (-87.22%)
Mutual labels: deep-reinforcement-learning, policy-gradient, ddpg, actor-critic
Easy Rl
强化学习中文教程，在线阅读地址：https://datawhalechina.github.io/easy-rl/
Stars: ✭ 3,004 (+859.74%)
Mutual labels: reinforcement-learning, deep-reinforcement-learning, policy-gradient, ddpg
Reinforcement Learning
Minimal and Clean Reinforcement Learning Examples
Stars: ✭ 2,863 (+814.7%)
Mutual labels: reinforcement-learning, deep-reinforcement-learning, policy-gradient, actor-critic
Tensorflow Reinforce
Implementations of Reinforcement Learning Models in Tensorflow
Stars: ✭ 480 (+53.35%)
Mutual labels: reinforcement-learning, deep-reinforcement-learning, policy-gradient, actor-critic
Lagom
lagom: A PyTorch infrastructure for rapid prototyping of reinforcement learning algorithms.
Stars: ✭ 364 (+16.29%)
Mutual labels: reinforcement-learning, deep-reinforcement-learning, policy-gradient, ddpg
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 (-29.07%)
Mutual labels: deep-reinforcement-learning, policy-gradient, ddpg, actor-critic
Reinforcement Learning With Tensorflow
Simple Reinforcement learning tutorials, 莫烦Python 中文AI教学
Stars: ✭ 6,948 (+2119.81%)
Mutual labels: reinforcement-learning, policy-gradient, actor-critic, ddpg
Pytorch Drl
PyTorch implementations of various Deep Reinforcement Learning (DRL) algorithms for both single agent and multi-agent.
Stars: ✭ 233 (-25.56%)
Mutual labels: reinforcement-learning, deep-reinforcement-learning, actor-critic, ddpg
Mlds2018spring
Machine Learning and having it Deep and Structured (MLDS) in 2018 spring
Stars: ✭ 124 (-60.38%)
Mutual labels: reinforcement-learning, policy-gradient, actor-critic
Policy Gradient
Minimal Monte Carlo Policy Gradient (REINFORCE) Algorithm Implementation in Keras
Stars: ✭ 135 (-56.87%)
Mutual labels: reinforcement-learning, deep-reinforcement-learning, policy-gradient
A2c
A Clearer and Simpler Synchronous Advantage Actor Critic (A2C) Implementation in TensorFlow
Stars: ✭ 169 (-46.01%)
Mutual labels: reinforcement-learning, policy-gradient, actor-critic
Pytorch Rl
Tutorials for reinforcement learning in PyTorch and Gym by implementing a few of the popular algorithms. [IN PROGRESS]
Stars: ✭ 121 (-61.34%)
Mutual labels: reinforcement-learning, policy-gradient, actor-critic
Minimalrl
Implementations of basic RL algorithms with minimal lines of codes! (pytorch based)
Stars: ✭ 2,051 (+555.27%)
Mutual labels: reinforcement-learning, deep-reinforcement-learning, ddpg
Reinforcementlearning Atarigame
Pytorch LSTM RNN for reinforcement learning to play Atari games from OpenAI Universe. We also use Google Deep Mind's Asynchronous Advantage Actor-Critic (A3C) Algorithm. This is much superior and efficient than DQN and obsoletes it. Can play on many games
Stars: ✭ 118 (-62.3%)
Mutual labels: reinforcement-learning, deep-reinforcement-learning, actor-critic
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 (+740.89%)
Mutual labels: reinforcement-learning, deep-reinforcement-learning, actor-critic
Deep-rl-mxnet
Mxnet implementation of Deep Reinforcement Learning papers, such as DQN, PG, DDPG, PPO
Stars: ✭ 26 (-91.69%)
Mutual labels: deep-reinforcement-learning, policy-gradient, ddpg
Hierarchical Actor Critic Hac Pytorch
PyTorch implementation of Hierarchical Actor Critic (HAC) for OpenAI gym environments
Stars: ✭ 116 (-62.94%)
Mutual labels: reinforcement-learning, deep-reinforcement-learning, actor-critic
Pytorch sac
PyTorch implementation of Soft Actor-Critic (SAC)
Stars: ✭ 174 (-44.41%)
Mutual labels: reinforcement-learning, deep-reinforcement-learning, actor-critic
OpenAI Lab 
NOTICE: Please use the next version, SLM-Lab.
An experimentation framework for Reinforcement Learning using OpenAI Gym, Tensorflow, and Keras.
OpenAI Lab is created to do Reinforcement Learning (RL) like science - theorize, experiment. It provides an easy interface to OpenAI Gym and Keras, with an automated experimentation and evaluation framework.
Features
- Unified RL environment and agent interface using OpenAI Gym, Tensorflow, Keras, so you can focus on developing the algorithms.
- Core RL algorithms implementations, with reusable modular components for developing deep RL algorithms.
- An experimentation framework for running hundreds of trials of hyperparameter optimizations, with logs, plots and analytics for testing new RL algorithms. Experimental settings are stored in standardized JSONs for reproducibility and comparisons.
- Automated analytics of the experiments for evaluating the RL agents and environments, and to help pick the best solution.
- The Fitness Matrix, a table of the best scores of RL algorithms v.s. the environments; useful for research.
With OpenAI Lab, we could focus on researching the essential elements of reinforcement learning such as the algorithm, policy, memory, and parameter tuning. It allows us to build agents efficiently using existing components with the implementations from research ideas. We could then test the research hypotheses systematically by running experiments.
Read more about the research problems the Lab addresses in Motivations. Ultimately, the Lab is a generalized framework for doing reinforcement learning, agnostic of OpenAI Gym and Keras. E.g. Pytorch-based implementations are on the roadmap.
Implemented Algorithms
A list of the core RL algorithms implemented/planned.
To see their scores against OpenAI gym environments, go to Fitness Matrix.
| algorithm | implementation | eval score (pending) |
|---|---|---|
| DQN | DQN | - |
| Double DQN | DoubleDQN | - |
| Dueling DQN | - | - |
| Sarsa | DeepSarsa | - |
| Off-Policy Sarsa | OffPolicySarsa | - |
| PER (Prioritized Experience Replay) | PrioritizedExperienceReplay | - |
| CEM (Cross Entropy Method) | next | - |
| REINFORCE | - | - |
| DPG (Deterministic Policy Gradient) off-policy actor-critic | ActorCritic | - |
| DDPG (Deep-DPG) actor-critic with target networks | DDPG | - |
| A3C (asynchronous advantage actor-critic) | - | - |
| Dyna | next | - |
| TRPO | - | - |
| Q*(lambda) | - | - |
| Retrace(lambda) | - | - |
| Neural Episodic Control (NEC) | - | - |
| EWC (Elastic Weight Consolidation) | - | - |
Run the Lab
Next, see Installation and jump to Quickstart.
Timelapse of OpenAI Lab, solving CartPole-v0.
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].