DeepRL-TensorFlow2
Reinforcement-Learning
| DeepRL-TensorFlow2 | Reinforcement-Learning | |
|---|---|---|
| 2 | 1 | |
| 573 | 4,091 | |
| - | - | |
| 0.0 | 0.0 | |
| almost 2 years ago | almost 4 years ago | |
| Python | Jupyter Notebook | |
| Apache License 2.0 | MIT License |
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DeepRL-TensorFlow2
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PPO implementation in TensorFlow2
I've been searching for a clean, good, and understandable implementation of PPO for continuous action space with TF2 witch is understandable enough for me to apply my modifications, but the closest thing that I have found is this code which seems to not work properly even on a simple gym cartpole env (discussed issues in git-hub repo suggest the same problem) so I have some doubts :). I was wondering whether you could recommend an implementation that you trust and suggest :)
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Question about using tf.stop_gradient in separate Actor-Critic networks for A2C implementation for TF2
I have been looking at this implementation of A2C. Here the author of the code uses stop_gradient only on the critic network at L90 bur not in the actor network L61 for the continuous case. However , it is used both in actor and critic networks for the discrete case. Can someone explain me why?
Reinforcement-Learning
What are some alternatives?
soft-actor-critic - Re-implementation of Soft-Actor-Critic (SAC) in TensorFlow 2.0
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).
tensorforce - Tensorforce: a TensorFlow library for applied reinforcement learning
FinRL-Library - Deep Reinforcement Learning Framework to Automate Trading in Quantitative Finance. NeurIPS 2020 & ICAIF 2021. 🔥 [Moved to: https://github.com/AI4Finance-Foundation/FinRL]
TensorFlow2.0-for-Deep-Reinforcement-Learning - TensorFlow 2.0 for Deep Reinforcement Learning. :octopus:
Stock-Prediction-Models - Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations
ydata-synthetic - Synthetic data generators for tabular and time-series data
snakeAI - testing MLP, DQN, PPO, SAC, policy-gradient by snake
minimalRL - Implementations of basic RL algorithms with minimal lines of codes! (pytorch based)
TradingGym - Trading Gym is an open source project for the development of reinforcement learning algorithms in the context of trading.
machin - Reinforcement learning library(framework) designed for PyTorch, implements DQN, DDPG, A2C, PPO, SAC, MADDPG, A3C, APEX, IMPALA ...
TextWorld - TextWorld is a sandbox learning environment for the training and evaluation of reinforcement learning (RL) agents on text-based games.