tensorforce
action-branching-agents
| tensorforce | action-branching-agents | |
|---|---|---|
| 1 | 2 | |
| 3,281 | 105 | |
| 0.2% | - | |
| 3.0 | 0.0 | |
| 25 days ago | about 1 year ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
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tensorforce
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Advice on doing RL for Settlers of Catan?
The most promising approach has been using the TensorForce framework (https://github.com/tensorforce/tensorforce) with a custom environment that represents a simpler game (1v1 against a bot that chooses actions randomly, no trading between players, and fixing discarding to be done automatically and at random).
action-branching-agents
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Large Action Spaces
Exactly, multiple action heads. There are some works that try this for DQN as https://arxiv.org/abs/1711.08946. However, i have not tried since I tend to prefer actor-critic methods.
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(Newbie question)How to solve using reinforcement learning 2x2 rubik's cube which has 2^336 states without ValueError?
That's a larger number than the number of atoms in the Universe. You need some kind of branching to limit the actions. Check out: https://github.com/atavakol/action-branching-agents
What are some alternatives?
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).
TensorFlow2.0-for-Deep-Reinforcement-Learning - TensorFlow 2.0 for Deep Reinforcement Learning. :octopus:
ai-traineree - PyTorch agents and tools for (Deep) Reinforcement Learning
DeepRL-TensorFlow2 - 🐋 Simple implementations of various popular Deep Reinforcement Learning algorithms using TensorFlow2
trax - Trax — Deep Learning with Clear Code and Speed
agents - TF-Agents: A reliable, scalable and easy to use TensorFlow library for Contextual Bandits and Reinforcement Learning.
acme - A library of reinforcement learning components and agents
deepdrive - Deepdrive is a simulator that allows anyone with a PC to push the state-of-the-art in self-driving
Deep-Reinforcement-Learning-in-Large-Discrete-Action-Spaces - PyTorch implementation of the paper "Deep Reinforcement Learning in Large Discrete Action Spaces" (Gabriel Dulac-Arnold, Richard Evans, Hado van Hasselt, Peter Sunehag, Timothy Lillicrap, Jonathan Hunt, Timothy Mann, Theophane Weber, Thomas Degris, Ben Coppin).