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deep-reinforcement-learning - Repo for the Deep Reinforcement Learning Nanodegree program
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 reinforcement-learning reinforcement-learning-algorithms neural-networks pytorch pytorch-rl ddpg dqn ppo dynamic-programming cross-entropy hill-climbing ml-agents openai-gym-solutions openai-gym rl-algorithmsReinforcement-learning-with-tensorflow - Simple Reinforcement learning tutorials
In these tutorials for reinforcement learning, it covers from the basic RL algorithms to advanced algorithms developed recent years. If you speak Chinese, visit 莫烦 Python or my Youtube channel for more.
reinforcement-learning tutorial q-learning sarsa sarsa-lambda deep-q-network a3c ddpg policy-gradient dqn double-dqn prioritized-replay dueling-dqn deep-deterministic-policy-gradient asynchronous-advantage-actor-critic actor-critic tensorflow-tutorials proximal-policy-optimization ppo machine-learningDeep-Reinforcement-Learning-for-Automated-Stock-Trading-Ensemble-Strategy-ICAIF-2020 - Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategy
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.
deep-reinforcement-learning openai-gym sharpe-ratio ddpg stock-trading ppo a2c-algorithm ensemble-strategy stock-trading-strategy automated-stock-tradingElegantRL - Lightweight, efficient and stable implementations of deep reinforcement learning algorithms using PyTorch
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).
lightweight reinforcement-learning gae deep-reinforcement-learning efficient pytorch stable dqn ddpg sac ppo td3 soft-actor-critic model-free-rl drl-pytorch bipedalwalkerhardcoreDeepRL - Modularized Implementation of Deep RL Algorithms in PyTorch
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.
deep-reinforcement-learning rainbow pytorch dqn ddpg double-dqn dueling-network-architecture quantile-regression option-critic-architecture deeprl categorical-dqn ppo a2c prioritized-experience-replay option-critic td3openai_lab - An experimentation framework for Reinforcement Learning using OpenAI Gym, Tensorflow, and Keras
NOTICE: Please use the next version, SLM-Lab. An experimentation framework for Reinforcement Learning using OpenAI Gym, Tensorflow, and Keras.
keras tensorflow openai experiment policy-gradient actor-critic ddpg deep-reinforcement-learning reinforcement-learning gym lab reinforcement learningpytorch-ddpg-naf - Implementation of algorithms for continuous control (DDPG and NAF).
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 deep-learning pytorch ddpg deep-deterministic-policy-gradientReinforcement-Learning - 🤖 Implements of Reinforcement Learning algorithms.
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.
reinforcement-learning reinforcement-learning-algorithms papers dqn ddpg deep-reinforcement-learning deep-learning-algorithmstrain-robot-arm-from-scratch - Build environment and train a robot arm from scratch (Reinforcement Learning)
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.
reinforcement-learning reinforcement-learning-excercises tensorflow robot-arm tutorial machine-learning ddpgrl_implementations
Some reinforcement learning algorithms I'm (re)-implementing, all in one place. Also is a dumping ground for other ML work.
reptile reinforcement-learning policy-gradient dqn deep-learning deep-q-network deep-q-learning deep-reinforcement-learning lstm ddpg machine-learning meta-learning hierarchical-reinforcement-learning a2c actor-criticDeep-Reinforcement-Learning-With-Python - Master classic RL, deep RL, distributional RL, inverse RL, and more using OpenAI Gym and TensorFlow with extensive Math
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.
reinforcement-learning deep-learning deep-reinforcement-learning openai-gym q-learning dqn policy-gradient a3c ddpg sac inverse-reinforcement-learning actor-critic bellman-equation double-dqn trpo c51 ppo a2c td3
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