gopala-kr / drl-agents Goto Github PK

• RL Landscape
• RL History
• RL Agents Implementation
  • Value Optimization Agents
  • Policy Optimization Agents
  • General Agents
  • Imitation Learning Agents
  • Hierarchical Reinforcement Learning Agents
  • Memory Types
  • Exploration Techniques
• RL Environments
• RL Mechanisms
• RL Applications
  • RL Games
  • DRL applied to Robotics
  • DRL applied to NLP
  • DRL applied to Vision
• References
  • Reference Implementations
  • Review Papers
  • RL Platforms
  • Deep Reinforcement Learning Papers

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Source: eleurent/phd-bibliography

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• Value Optimization
  • [QR-DQN]
  • [DQN] - [Slides] [Code] [rainbow]
  • [Bootstrapped DQN]
  • [DDQN]
  • [NEC]
  • [MMC]
  • [N-step Q Learning]
  • [PAL]
  • [Categorical DQN]
  • [NAF]
• Policy Optimization
  • [Policy Gradient]
  • [Actor Critic]
    • [DDPG] [Code]
      • [HAC DDPG]
      • [DDPG with HER]
    • [Clipped PPO]
    • [PPO]
• [DFP]
• Imitation
  • [Behavioural cloning]
  • [Inverse Reinforcement Learning] [Code] [irl-imitation-code]
  • [Generative Adversarial Imitation Learning]

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• Deep Q Network (DQN) (code)
• Double Deep Q Network (DDQN) (code)
• Dueling Q Network
• Mixed Monte Carlo (MMC) (code)
• Persistent Advantage Learning (PAL) (code)
• Categorical Deep Q Network (C51) (code)
• Quantile Regression Deep Q Network (QR-DQN) (code)
• N-Step Q Learning | Distributed (code)
• Neural Episodic Control (NEC) (code)
• Normalized Advantage Functions (NAF) | Distributed (code)

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• Policy Gradients (PG) | Distributed (code)
• Asynchronous Advantage Actor-Critic (A3C) | Distributed (code)
• Deep Deterministic Policy Gradients (DDPG) | Distributed (code)
• Proximal Policy Optimization (PPO) (code)
• Clipped Proximal Policy Optimization (CPPO) | Distributed (code)
• Generalized Advantage Estimation (GAE) (code)

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• Direct Future Prediction (DFP) | Distributed (code)

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• Behavioral Cloning (BC) (code)

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• Hierarchical Actor Critic (HAC) (code)

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• Hindsight Experience Replay (HER) (code)
• Prioritized Experience Replay (PER) (code)

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• E-Greedy (code)
• Boltzmann (code)
• Ornstein–Uhlenbeck process (code)
• Normal Noise (code)
• Truncated Normal Noise (code)
• Bootstrapped Deep Q Network (code)
• UCB Exploration via Q-Ensembles (UCB) (code)
• Noisy Networks for Exploration (code)

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• Temporal difference(TD) learning (1988)
• Q‐learning (1998)
• BayesRL (2002)
• RMAX (2002)
• CBPI (2002)
• PEGASUS (2002)
• Least‐Squares Policy Iteration (2003)
• Fitted Q‐Iteration (2005)
• GTD (2009)
• UCRL (2010)
• REPS (2010)
• DQN (2014) - DeepMind

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• [Acrobot]
• [Bike]
• [Blackjack]
• [Cartpole]
• [ContextBandit]
• [Continuous Chain]
• [Corridor]
• [Discrete Chain]
• [Discretiser (for continuous environments)]
• [Double Loop]
• [Environment]
• [Gridworld]
• [Inventory management]
• [Linear context bandit]
• [Linear dynamic quadratic]
• [Mountaincar (2d and 3d)]
• [POMDP Maze]
• [Optimistic Task]
• [Puddleworld]
• [Random MDPs]
• [Riverswim]

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• [Attention and Memory]
• [Unsupervised learning ]
  • [GANs]
  • [GQN]
  • [UNREAL]
• [Hierarchical RL]
  • [FuNs]
  • [Option-Critic]
  • [STRAW]
  • [h-DQN]
  • [Stochastic Neural Networks]
• [Multi-agent RL]
• [Relational RL]
• [Learning to Learn, a.k.a. Meta-Learning]
  • [Few/One/Zero-shot Learning]
    • [MAML]
  • [Transfer and Multi-Task Learning]
  • [Learning to Optimize]
  • [Learning to Re-inforcement Learn]
  • [Learning Combinatorial Optimization]
  • [AutoML]

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• Chinook (1997;2007) for Checkers,
• Deep Blue (2002) for chess,
• Logistello (1999) for Othello,
• TD-Gammon (1994) for Backgammon,
• GIB (2001) for contract bridge,
• MoHex (2017) for Hex,
• DQN (2016)(2018) for Atari 2600 games,
• AlphaGo (2016a) and AlphaGo Zero (2017) for Go,
• Alpha Zero (2017) for chess, shogi, and Go,
• Cepheus (2015), DeepStack (2017), and Libratus (2017a;b) for heads-up Texas Hold’em Poker,
• Jaderberg et al. (2018) for Quake III Arena Capture the Flag,
• OpenAI Five, for Dota 2 at 5v5, https://openai.com/five/,
• Zambaldi et al. (2018), Sun et al. (2018), and Pang et al. (2018) for StarCraft II

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• [Board Games]
  • [Computer Go]
  • [AlphaGo: Trainig pipeline with MCTS]
  • [AlphaGo Zero]
  • [Alpha Zero]
• [Card Games]
  • [DeepStack]
• [Video Games]
  • [Atari 2600 games]
  • [StarCraft]
  • [StarCraft II mini-games]
  • [Quake III Arena]
  • [Minecraft]
  • [Super Smash Bros]
  • [Doom]
  • [ViZDoom]

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• [Sim-to-Real]
  • [MuJoCo]
• [Imitation Learning]
• [Value-based Learning]
• [Policy-based Learning]
• [Model-based Learning]
• [Autonomous Driving Vehicles]

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• [Sequence Generation]
• [Machine Translation]
• [Dialogue Systems]

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• [Recognition]
• [Motion Analysis]
• [Scene Understanding]
• [Vision + NLP]
• [Visual Control]
• [Interactive Perception]

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• reference implementations
• review papers
• RL platforms
• deep-reinforcement-learning-papers
• ICLR2019-RL-Papers

• deepmind.com/blog
• blog.openai
• openai/spinningup
• DeepRLHacks-Deep RL Bootcamp (Aug 2017)
• Deep RL Bootcamp
• UC Berkeley: Deep Reinforcement Learning
• MIT 6.S094: Deep Learning for Self-Driving Cars
• Deep Reinforcement Learning and Control Spring 2017, CMU 10703
• Sutton & Barto's: Reinforcement Learning: An Introduction
• Algorithms for Reinforcement Learning
• reinforcejs
• Hands-On-Reinforcement-Learning-With-Python
• jetson-reinforcement
• DEEP REINFORCEMENT LEARNING
• Reinforcement Learning Applications
• Lessons Learned Reproducing a Deep Reinforcement Learning Paper
• Scalable Deep Reinforcement Learning for Robotic Manipulation
• Closing the Simulation-to-Reality Gap for Deep Robotic Learning
• Intel AI : demystifying-deep-reinforcement-learning
• Intel AI : deep-reinforcement-learning-with-neon
• Intel AI : reinforcement-learning-coach-intel
• eecs.berkeley.deeprlcourse
• Deep Learning for Video Game Playing
• Deep Reinforcement Learning that Matters
• Adversarial Examples: Attacks and Defenses for Deep Learning
• Reinforcement learning
• A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress
• A Survey of Knowledge Representation and Retrieval for Learning in Service Robotics
• Applications of Deep Reinforcement Learning in Communications and Networking: A Survey
• An Introduction to Deep Reinforcement Learning
• Challenges of Real-World Reinforcement Learning
• Modern Deep Reinforcement Learning Algorithms

Maintainer

Gopala KR / @gopala-kr