Deep Reinforcement Learning (DRL)

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• Reinforcement Learning (RL)
  • Monte Carlo (MC) Method - Model Free Reinforcement Learning
  • Markov Decision Process (MDP)
  • State-Action-Reward-State-Action (SARSA)
  • Q Learning
    • Deep Q Network (DQN)
  • Deep Reinforcement Learning (DRL) DeepRL
  • Distributed Deep Reinforcement Learning (DDRL)
  • Evolutionary Computation / Genetic Algorithms
  • Actor Critic
    • Asynchronous Advantage Actor Critic (A3C)
    • Advanced Actor Critic (A2C)
    • Lifelong Latent Actor-Critic (LILAC)
  • Hierarchical Reinforcement Learning (HRL)
• MERLIN: Inside Out - Curious Optimistic Reasoning
• OpenAI Gym
• Policy ... Policy vs Plan ... Constitutional AI ... Trust Region Policy Optimization (TRPO) ... Policy Gradient (PG) ... Proximal Policy Optimization (PPO)

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• Introduction to Various Reinforcement Learning Algorithms. Part I (Q-Learning, SARSA, DQN, DDPG) | Steeve Huang
• Introduction to Various Reinforcement Learning Algorithms. Part II (TRPO, PPO) | Steeve Huang
• Guide

Goal-oriented algorithms, which learn how to attain a complex objective (goal) or maximize along a particular dimension over many steps; for example, maximize the points won in a game over many moves. Reinforcement learning solves the difficult problem of correlating immediate actions with the delayed returns they produce. Like humans, reinforcement learning algorithms sometimes have to wait a while to see the fruit of their decisions. They operate in a delayed return environment, where it can be difficult to understand which action leads to which outcome over many time steps.

Importance Weighted Actor-Learner Architecture (IMPALA)

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• Artificial General Intelligence Is Here, and Impala Is Its Name | Aaron Krumins
• DeepMind Lab
• IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
• Importance Weighted Actor-Learner Architectures: Scalable Distributed DeepRL in DMLab-30

uses resources more efficiently in single-machine training but also scales to thousands of machines without sacrificing data efficiency or resource utilisation. We achieve stable learning at high throughput by combining decoupled acting and learning with a novel off-policy correction method called V-trace. IMPALA is able to achieve better performance than previous agents with less data, and crucially exhibits positive transfer between tasks as a result of its multi-task approach.

DMLab-30

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DMLab-30 is a collection of new levels designed using our open source RL environment DeepMind Lab. These environments enable any DeepRL researcher to test systems on a large spectrum of interesting tasks either individually or in a multi-task setting.

• DMLab-30 | GitHub
• IMPALA distributed agent in DMLab-30
• IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures