Displaying 1 to 11 from 11 results

deep-reinforcement-learning - Repo for the Deep Reinforcement Learning Nanodegree program

  •    Jupyter

This repository contains material related to Udacity's Deep Reinforcement Learning Nanodegree program. The tutorials lead you through implementing various algorithms in reinforcement learning. All of the code is in PyTorch (v0.4) and Python 3.

Deep-Reinforcement-Learning-for-Automated-Stock-Trading-Ensemble-Strategy-ICAIF-2020 - Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategy

  •    Jupyter

This ensemble strategy is reimplemented in a Jupiter Notebook at FinRL. Stock trading strategies play a critical role in investment. However, it is challenging to design a profitable strategy in a complex and dynamic stock market. In this paper, we propose a deep ensemble reinforcement learning scheme that automatically learns a stock trading strategy by maximizing investment return. We train a deep reinforcement learning agent and obtain an ensemble trading strategy using the three actor-critic based algorithms: Proximal Policy Optimization (PPO), Advantage Actor Critic (A2C), and Deep Deterministic Policy Gradient (DDPG). The ensemble strategy inherits and integrates the best features of the three algorithms, thereby robustly adjusting to different market conditions. In order to avoid the large memory consumption in training networks with continuous action space, we employ a load-on-demand approach for processing very large data. We test our algorithms on the 30 Dow Jones stocks which have adequate liquidity. The performance of the trading agent with different reinforcement learning algorithms is evaluated and compared with both the Dow Jones Industrial Average index and the traditional min-variance portfolio allocation strategy. The proposed deep ensemble scheme is shown to outperform the three individual algorithms and the two baselines in terms of the risk-adjusted return measured by the Sharpe ratio.

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).




DeepRL - Modularized Implementation of Deep RL Algorithms in PyTorch

  •    Python

If you have any question or want to report a bug, please open an issue instead of emailing me directly. Modularized implementation of popular deep RL algorithms in PyTorch. Easy switch between toy tasks and challenging games.

pytorch-ddpg-naf - Implementation of algorithms for continuous control (DDPG and NAF).

  •    Python

This repository is deprecated and is no longer maintaned. Please see a more recent implementation of RL for continuous control at jax-sac. Reimplementation of Continuous Deep Q-Learning with Model-based Acceleration and Continuous control with deep reinforcement learning.

Reinforcement-Learning - 🤖 Implements of Reinforcement Learning algorithms.

  •    Python

This repo is implements of Reinforcement Learning Algorithms, implementing as learning, some of them are even another version of some tutorial. Any contributions are welcomed. Deep Deterministic Policy Gradient (DDPG) Implement of DDPG.


train-robot-arm-from-scratch - Build environment and train a robot arm from scratch (Reinforcement Learning)

  •    Python

This Reinforcement Learning practice code has its Chinese tutorial on 莫烦Python. You can view more tutorials on this page or know more about me on here.

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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