Deep-Reinforcement-Learning-Algorithms
DeepRL-TensorFlow2
| Deep-Reinforcement-Learning-Algorithms | DeepRL-TensorFlow2 | |
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| 3 | 2 | |
| 580 | 573 | |
| - | - | |
| 3.6 | 0.0 | |
| almost 3 years ago | almost 2 years ago | |
| Jupyter Notebook | Python | |
| - | Apache License 2.0 |
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Deep-Reinforcement-Learning-Algorithms
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Is there a canonical simple "helloworld" neural network design? Something beyond AND/OR logic, a handful of nodes that does something mildly "useful"?
I guess the most spectacular in terms of performance/"brain size" ratio is a 2 neuron, 8 weights network https://github.com/Rafael1s/Deep-Reinforcement-Learning-Algorithms/tree/master/CartPole-Policy-Based-Hill-Climbing
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Training time of CartPole is way to long
It can be solved in 113 episodes by Hill Climbing algorithm, https://github.com/Rafael1s/Deep-Reinforcement-Learning-Algorithms/tree/master/CartPole-Policy-Based-Hill-Climbingor by Double Deep Q-Learning in 612 episodes, https://github.com/Rafael1s/Deep-Reinforcement-Learning-Algorithms/tree/master/Cartpole-Double-Deep-Q-Learning
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Need help with PyTorch script for Actor_Critic implementation of MountainCar env.
You can find the solution for MountainCar env here: https://github.com/Rafael1s/Deep-Reinforcement-Learning-Algorithms/tree/master/MountainCarContinuous-TD3This solution implemented using PyTorch. The TD3 model is the successor to DDPG algorithm using the Actor-Critic model.
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?
What are some alternatives?
ElegantRL - Massively Parallel Deep Reinforcement Learning. 🔥
soft-actor-critic - Re-implementation of Soft-Actor-Critic (SAC) in TensorFlow 2.0
Deep-Reinforcement-Learning-for-Automated-Stock-Trading-Ensemble-Strategy-ICAIF-2020 - Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategy. ICAIF 2020. Please star. [Moved to: https://github.com/AI4Finance-Foundation/Deep-Reinforcement-Learning-for-Automated-Stock-Trading-Ensemble-Strategy-ICAIF-2020]
tensorforce - Tensorforce: a TensorFlow library for applied reinforcement learning
Popular-RL-Algorithms - PyTorch implementation of Soft Actor-Critic (SAC), Twin Delayed DDPG (TD3), Actor-Critic (AC/A2C), Proximal Policy Optimization (PPO), QT-Opt, PointNet..
TensorFlow2.0-for-Deep-Reinforcement-Learning - TensorFlow 2.0 for Deep Reinforcement Learning. :octopus:
tianshou - An elegant PyTorch deep reinforcement learning library.
ydata-synthetic - Synthetic data generators for tabular and time-series data
rl_lib - Series of deep reinforcement learning algorithms 🤖
minimalRL - Implementations of basic RL algorithms with minimal lines of codes! (pytorch based)
machin - Reinforcement learning library(framework) designed for PyTorch, implements DQN, DDPG, A2C, PPO, SAC, MADDPG, A3C, APEX, IMPALA ...
tf2multiagentrl - Clean implementation of Multi-Agent Reinforcement Learning methods (MADDPG, MATD3, MASAC, MAD4PG) in TensorFlow 2.x