An AI Learns to Play Starcraft 2 with Reinforcement Learning
Interesting video tinkering with reinforcement learning via Stable Baselines 3 and Starcraft 2. Code and model: https://github.com/Sentdex/SC2RL
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Deep Q-Network Solves Cart and Pole – Reinforcement Learning Code Project
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Frank
- March 28, 2022
- agent environment
- AI
- AlphaGo
- artificial intelligence
- artificial neural network
- Bellman equation
- CNN
- Deep Learning
- Deep Q-network
- DQN
- Education
- experience replay
- Machine Learning
- markov decision process
- MDP
- Neural Network
- OpenAI Five
- OpenAI Gym
- policy gradients
- policy network
- Python
- PyTorch
- Q-learning
- Q-value
- Reinforcement Learning
- replay memory
- SGD
- stochastic gradient descent
- Supervised Learning
- TensorFlow
- Tutorial
- Unsupervised Learning
In this episode, learn how to use a deep Q-network to solve the Cart and Pole environment.
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AI Learns Insane Monopoly Strategies
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Frank
- January 3, 2022
- AI
- awesome
- B2 AI
- b2studios
- b2studios ai
- b2studios board game ai
- b2studios monopoly
- best set monopoly
- board game ai
- bot learns monopoly
- brown set b2studios
- browns best set
- buy the browns
- Cool
- how to play monopoly
- monopoly
- monopoly ai
- monopoly bot
- monopoly bot learns
- monopoly brown set op
- monopoly gameplay
- monopoly NEAT
- monopoly neat AI
- monopoly strategy
- NEAT
- neuro evolution
- perfect monopoly
- play monopoly
- strategies
- YouTube
11.2 million games of self-play were used to discover the secrets of this classic game Download this AI: https://github.com/b2developer/MonopolyNEAT
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Can AI Learn to Cooperate?
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Frank
- April 9, 2021
- actor critic methods
- cooperative reinforcement learning
- ddpg
- deep deterministic policy gradients
- maddpg
- maddpg algorithm
- maddpg openai gym
- maddpg pytorch
- maddpg tutorial
- multi agent actor critic
- multi agent actor critic algorithm
- multi agent actor critic explained
- multi agent actor critic tutorial
- multi agent actor deep deterministic policy gradients
- multi agent deep deterministic policy gradients
- multi agent reinforcement learning
- policy gradients
Machine Learning with Phil covers Multi Agent Deep Deterministic Policy Gradients (MADDPG) in this video. Multi agent deep deterministic policy gradients is one of the first successful algorithms for multi agent artificial intelligence. Cooperation and competition among AI agents is going to be critical as applications of deep learning expand in our daily lives. In […]
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DeepMind’s AI Watches YouTube and Learns To Play
Two Minute Papers looks at the paper “Playing hard exploration games by watching YouTube.”
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A Beginner’s Guide to Reinforcement Learning
Reinforcement learning is one of the most exciting collections of techniques for building self-learning systems. Over the past five years, we’ve seen RL successfully meet such challenges as exceeding human performance on popular video games and board games. Despite the excitement around the success of these RL agents, it has remained extraordinarily difficult for most […]
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Reinforcement Learning: Machine Learning Meets Control Theory
Steve Brunton explains reinforcement learning. Reinforcement learning is a powerful technique at the intersection of machine learning and control theory, and it is inspired by how biological systems learn to interact with their environment. In this video, we provide a high level overview of reinforcement learning, along with leading algorithms and impressive applications.
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New Reinforcement Learning Methods to Improve AI in Gaming and Beyond
This episode of the AI show covers reinforcement learning – an AI approach that is especially promising for training naturally behaving game characters with nuanced reactions to human players. Jump To: [00:50] What is reinforcement learning[02:26] Project Malmo – Minecraft as a platform for AI research[05:00] Project Paidia: towards RL agents that learn to collaborate […]
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AI-Driven Basketball Players Dribble Like Mad
Two Minute Papers explains the paper “Local Motion Phases for Learning Multi-Contact Character Movements” in the video below.
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Provably Efficient Reinforcement Learning with Dr. Akshay Krishnamurthy
MSR’s New York City lab is home to some of the best reinforcement learning research on the planet but if you ask any of the researchers, they’ll tell you they’re very interested in getting it out of the lab and into the real world. One of those researchers is Dr. Akshay Krishnamurthy and today, he […]
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