Yuandong Tian at AI Frontiers : Planning in Reinforcement Learning
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Deep Reinforcement Learning (DRL) has made strong progress in many tasks, such as board games, robotics, navigation, neural architecture search, etc. I will present our recent open-sourced DRL frameworks to facilitate game research and development. Our framework is scalable so we can can reproduce AlphaGoZero and AlphaZero using 2000 GPUs, achieving super-human performance of Go AI that beats 4 top-30 professional players. We also show usability of our platform by training agents in real-time strategy games, and show interesting behaviors with a small amount of resource.
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Yuandong Tian at AI Frontiers : Planning in Reinforcement Learning
- 1. Planning in Reinforcement Learning Yuandong Tian Research Scientist Facebook AI Research
- 2. AI works in a lot of situations Medical Translation Personalization Surveillance Object Recognition Smart Design Speech Recognition Board game
- 3. What AI still needs to improve Very few supervised data Complicated environments Lots of Corner cases. Home Robotics Autonomous Driving ChatBot StarCraftQuestion Answering Common Sense Exponential space to explore
- 4. What AI still needs to improve Human level A scary trend of slowing down “Man, we need more data” Initial Enthusiasm “It really works! All in AI!” Trying all possible hacks “How can that be…” Despair “No way, it doesn’t work” Performance Efforts
- 5. What AI still needs to improve Human level A scary trend of slowing down “Man, we need more data” Initial Enthusiasm “It really works! All in AI!” Trying all possible hacks “How can that be…” Despair “No way, it doesn’t work” Performance Efforts We need novel algorithms
- 6. Reinforcement Learning Action State Reward Agent Environment [R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction] Atari Games Go DoTA 2 Doom Quake 3 StarCraft
- 7. Why Planning is important? Just one example
- 8. AlphaGo Zero Update Models Generate Training data Self-Replays Zero-human knowledge [Silver et al, Mastering the game of Go without human knowledge, Nature 2017]
- 9. AlphaGo Zero Strength • 3 days version • 4.9M Games, 1600 rollouts/move • 20 block ResNet • Defeat AlphaGo Lee. • 40 days version • 29M Games, 1600 rollouts/move • 40 blocks ResNet. • Defeat AlphaGo Master by 89:11
- 10. ELF: Extensive, Lightweight and Flexible Framework for Game Research Larry ZitnickQucheng Gong Wenling Shang Yuxin WuYuandong Tian https://github.com/facebookresearch/ELF [Y. Tian et al, ELF: An Extensive, Lightweight and Flexible Research Platform for Real-time Strategy Games, NIPS 2017]
- 11. ELF: A simple for-loop Action State Reward Agent Environment C++ Python
- 12. How ELF works Game Threads (C++) 0 1 2 3 4 5 6 7 Batch BatchBatch Batch Batch Python
- 13. Distributed ELF Server Evaluate/Selfplay Training Send request (game params) Receive experiences Client Client Client Client Client Client Client AlphaGoZero (more synchronization) AlphaZero (less synchronization) Putting AlphaGoZero and AlphaZero into the same framework
- 14. ELF OpenGo • System can be trained with 2000 GPUs in 2 weeks. • Decent performance against professional players and strong bots. • Abundant ablation analysis • Decoupled design, code highly reusable for other games. We open source the code and the pre-trained model for the Go and ML community https://github.com/pytorch/ELF Simple tutorial in experimental branch (tutorial, tutorial_distri)
- 15. ELF OpenGo Performance 20-0Name (rank) ELO (world rank) Result Kim Ji-seok 3590 (#3) 5-0 Shin Jin-seo 3570 (#5) 5-0 Park Yeonghun 3481 (#23) 5-0 Choi Cheolhan 3466 (#30) 5-0 Single GPU, 80k rollouts, 50 seconds Offer unlimited thinking time for the players Vs top professional players Vs strong bot (LeelaZero) [158603eb, 192x15, Apr. 25, 2018]: 980 wins, 18 losses (98.2%) Vs professional players Single GPU, 2k rollouts, 27-0 against Taiwanese pros.
- 16. Planning is how new knowledge is created Game Start Game End Random Moves Meaningful Moves Already dan level even if the opening doesn’t make much sense. Planning #rollouts Win rate against bot without planning 50% 100% Training is almost always constrained by model capacity (why 40b > 20b) Where the reward signal is
- 17. Planning is how new knowledge is created T-step looking forward Tree searchOne-step looking forward Monte Carlo Sampling Learning a neural network that directly predicts the optimal value/policy Temporal Difference (TD) in Reinforcement Learning:
- 18. Planning is how game AI is created Extensive search/planning Evaluate Consequence Black wins White wins Black wins White wins Black wins Current game situation Lufei Ruan vs. Yifan Hou (2010) AlphaBeta Pruning + Iterative Deepening Monte Carlo Tree Search …
- 19. How to plan without a known model? If you don’t have a ground truth dynamics model … • Only (human/expert) trajectories, no world model. • Limited access of world models. • Cannot restart the model, cannot query any (s, a) pair • Noisy signals from the world model • … If you have a ground truth dynamics model … • Infinite access of the exact world model • May query any (s, a) pair • … Current state Next state Action to take Build one
- 20. Navigation Target Yi Wu Georgia Gkioxari Yuxin Wu How to plan the trajectory?
- 21. Build a semantic model outdoor living room sofa Find “oven” car chair dining room kitchen oven Incomplete model of the environment [Y. Wu et al, Learning and Planning with a Semantic Model, submitted to ICLR 2019]
- 22. Build a semantic model Bayesian Inference 𝑃(𝑧|𝑌) car chair dining room kitchen oven 0.7 0.95 0.8 0.5 0.6 0.7 Next step “kitchen” outdoor living room sofa Learning experience 𝑌
- 23. LEAPS LEArning and Planning with a Semantic model living room kitchen chair sofa Dining room 𝑃(𝑧kitchen,living room) 𝑃(𝑧sofa,living room) 𝑃(𝑧chair,living room) 𝑃(𝑧dining,living room) living room kitchen chair sofa Dining room 𝑃(𝑧kitchen,living room) 𝑃 𝑧sofa,living room 𝑌 = 1 𝑃(𝑧chair,living room) 𝑃(𝑧dining,living room) Planning the trajectory and more exploration Planning the trajectory and more exploration
- 24. House3D SUNCG dataset, 45K scenes, all objects are fully labeled. https://github.com/facebookresearch/House3D Depth Segmentation maskRGB image Top-down map
- 25. Learning the Prior between Different Rooms
- 26. Test Performance on ConceptNav
- 27. Case Study • A case study • Go to “outdoor” Prior: 𝑃(𝑧) Birth Outdoor Living room Garage 0.12 0.38 0.76 0.73 0.28 Sub-Goal: Outdoor
- 28. Case Study • A case study • Go to “outdoor” Sub-Goal: Outdoor Failed! Birth Outdoor Living room Garage 0.12 0.38 0.01 0.73 0.28 Posterior: 𝑃(𝑧|𝐸)
- 29. • A case study • Go to “outdoor” Posterior: 𝑃(𝑧|𝐸) Sub-Goal: Garage Birth Outdoor Living room Garage 0.12 0.01 0.73 0.28 Failed! Case Study 0.08
- 30. Case Study • A case study • Go to “outdoor” Sub-Goal: Living Room Posterior: 𝑃(𝑧|𝐸) Birth Outdoor Living room Garage 0.12 0.08 0.01 0.28 Success 0.99
- 31. Case Study • A case study • Go to “outdoor” Sub-Goal: Outdoor Posterior: 𝑃(𝑧|𝐸) Success Birth Outdoor Living room Garage 0.99 0.08 0.01 0.280.99
- 33. Iterative LQR What if the dynamics is nonlinear? New Policy Sample according to the new policy
- 34. Non-differentiable Plans • Direct predicting combinatorial solutions. [O. Vinyals. et al, Pointer Networks, NIPS 2015] Convex hull Seq2seq model [H. Mao et al, Resource Management with Deep Reinforcement Learning, ACM Workshop on Hot Topics in Networks, 2016] Schedule the job to i-th slot Policy gradient
- 35. Neural network rewriter st Sample 𝒈 𝒕 ∼ 𝑺𝑷 𝒈 𝒕 , 𝒈 𝒕 ⊂ 𝒔 𝒕 Sample 𝒂 𝒕 ∼ 𝑹𝑺 𝒈 𝒕 𝒔 𝒕+𝟏 = 𝒇(𝒔 𝒕, 𝒈 𝒕, 𝒂 𝒕) Input Encoder Score Predictor Rule Selector [X. Chen and Y. Tian, Learning to Progressively Plan, submitted to ICLR 2019] Is it simpler to improve the solution progressively?
- 36. Job Scheduling Scheduling 1 2 3 T=2, S=1 T=3, S=2 T=1, S=3 0 1 2 3 1 32 4 5 6 0 1 2 3 1 2 3 1 32 4 5 6 1 2 3 Graph representationJobs 10 Resource 1 Resource 2 time time Slow down Slow down
- 37. Job Scheduling g2 0.1 g4 0.7 g5 0.2 Input Encoder Score Predictor Rule Selector 0.1 -0.3 0.2 DAG-LSTM Embedding 0 1 5 3 42 FC FC FC Softmax St at St+1 0 1 5 3 42 -0.2 0.5 𝐠 𝐭 0 1 5 4 32
- 38. Job Scheduling #Resources 2 5 10 20 Shortest Job First 4.80 5.83 5.58 5.00 Shortest First Search 4.25 5.05 5.54 4.98 DeepRM 2.81 6.52 9.20 10.18 Neural Rewriter (Ours) 2.80 3.36 4.50 4.63 Optim (LB) 2.57 3.02 4.08 4.26 Earliest Job First (UB) 11.11 13.62 22.13 24.23
- 39. Expression Simplification a1 0 … a5 1 (Constant Reduction) … a19 0 Input Encoder Score Predictor Rule Selector Embedding Tree-LSTM FC 0 -1 0.7 FC Softmax St St+1 𝐠 𝐭 at <= min - v0 v2 v1 v1 <= min - v0 v2 v1 v1 <= min 0 v0 v2
- 40. Expression Simplification Expr Len Reduction Tree Size Reduction Halide Rule-based Rewriter 36.13 9.68 Heuristic Search 43.27 12.09 Neural Rewriter (Ours) 46.98 13.53
- 41. Example ≤ 5 max v 3 + 3 5 ≤ max 𝑣, 3 + 3 ≤ 5 v 3 max + + 33 ≤ 5 v 3 + ≤ 5 3 3 + OR ≤ 5 v 3 + True OR True
- 42. Future Directions Multi-Agent Admiral (General) Captain Lieutenant Hierarchical RL RL Systems Model-based RL Policy Optimization Model Estimation RL applications RL for Optimization
- 43. How to do well in Reinforcement Learning? Experience on (distributed) systems Strong math skills Strong coding skills Parameter tuning skills
- 44. Thanks!
Notas do Editor
- RL is one of the methods that might come to rescue. The basic idea of RL is very simple. We have an agent who perceives the state from the environment, takes an action and receives a reward. The environment receives the action, changes its internal state and repeats. With virtual environments, we could potentially get infinite amount of data to train our models. Since then, deep reinforcement learning has make substantial progress in different kind of games, including Atari games, Go, DoTA 2, etc. Then the question is, what is the next step? In this talk, I am going to talk about a few recent works that mainly explore the power of planning in reinforcement learning setting.
- 1.
- I will start with one example why planning is important.
- We all know that last year, DeepMind has released a paper in Nature about AlphaGoZero, which learns super-human Go bot without any human knowledge. The idea is very simple, starting from random generated models, we use planning algorithms like MCTS to find the best moves in each step, and save the selfplay into a replay buffer. Then we update the models based on the self-replay buffer. Then repeat.
- This approach is surprisingly simple, yet it gives very strong performance. 3 days with thousands of TPUs and the model can already beat AlphaGo Lee, after 40 days it defeats AlphaGo Master.
- Inspired by this interesting and exciting results, we thus try reproducing AlphaGoZero with our recent published ELF platform. The goal is to understand why such a simple approach yields a strong performance.
- ELF is an Extensive, Lightweight and Flexible framework that makes a practical RL system easy and manageable. For this, ELF puts all the implementation and design details into the C++ side, leaving the Python side a simple for-loop, as advertised in the text book. Moreover, each time the python side returns a batch for a neural network model to operate on, improving its efficiency.
- The key idea of ELF is to achieve dynamic batching from multiple game instances. In this platform, many games are running simultaneously. From time to time each have requests to call deep learning API (like PyTorch) for computing the next states and actions, or store the game history into replay buffers. ELF provides a dynamic batching interface so that requests can be automatically batched for high thoroughput.
- We improve our framework to support distributed setting, and putting AGZ and AZ together.
- We then releases ELF OpenGo, reproduces AZ and AGZ.
- One question is that, ok, what can we learn from it?
- One interesting observation from this experiment is that the knowledge is propagated backwards. You start to see meaningful moves from the end of the game, where there is reward signal. Over iterations, the meaningful moves are backpropagated all the way to the opening of the game. The right figure shows that even with well-trained models, still with the additional planning the strength is much higher. In fact, the strength of the bot is always constrained by the capacity of the model. We are working on an arXiv paper to discuss about the training details.
- Not only training AGZ/AZ requires planning. For general reinforcement learning, during training, planning is also very important. This not only includes 1 step look forward, but also including T-step looking forward as well as more complicated planning mechanism like tree search. A large portion of DRL is to try pushing the results given by planning into neural networks.
- Planning is also very important for general game AI. Chess or other games are other important examples.
- Traditional