ai-traineree
action-branching-agents
| ai-traineree | action-branching-agents | |
|---|---|---|
| 1 | 2 | |
| 24 | 105 | |
| - | - | |
| 0.0 | 0.0 | |
| about 2 years ago | about 1 year ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
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ai-traineree
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Rainbow Library
Maybe you'll find this easy to use https://github.com/laszukdawid/ai-traineree
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?
dopamine - Dopamine is a research framework for fast prototyping of reinforcement learning algorithms.
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).
autonomous-learning-library - A PyTorch library for building deep reinforcement learning agents.
tensorforce - Tensorforce: a TensorFlow library for applied reinforcement learning
DQN-Atari - Deep Q-Learning (DQN) implementation for Atari pong.
trax - Trax — Deep Learning with Clear Code and Speed
machin - Reinforcement learning library(framework) designed for PyTorch, implements DQN, DDPG, A2C, PPO, SAC, MADDPG, A3C, APEX, IMPALA ...
acme - A library of reinforcement learning components and agents
ElegantRL - Massively Parallel Deep Reinforcement Learning. 🔥
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).