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 ## List of papers
 
 * [Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model](https://shagunsodhani.com/papers-I-read/Mastering-Atari,-Go,-Chess-and-Shogi-by-Planning-with-a-Learned-Model)
+* [Contrastive Learning of Structured World Models](https://shagunsodhani.com/papers-I-read/Contrastive-Learning-of-Structured-World-Models)
 * [Gossip based Actor-Learner Architectures for Deep RL](https://shagunsodhani.com/papers-I-read/Gossip-based-Actor-Learner-Architectures-for-Deep-RL)
 * [How to train your MAML](https://shagunsodhani.com/papers-I-read/How-to-train-your-MAML)
 * [PHYRE - A New Benchmark for Physical Reasoning](https://shagunsodhani.com/papers-I-read/PHYRE-A-New-Benchmark-for-Physical-Reasoning)
diff --git a/site/_posts/2019-11-28-Contrastive Learning of Structured World Models.md b/site/_posts/2019-11-28-Contrastive Learning of Structured World Models.md
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+---
+layout: post
+title: Contrastive Learning of Structured World Models
+comments: True
+excerpt: 
+tags: ['2019', 'Graph Neural Network', 'Object-Oriented Learning', 'Relational Learning', AI, Graph, GNN]
+
+---
+
+## Introduction
+
+* The paper introduces Contrastively-trained Structured World Models (C-SWMs).
+
+* These models use a contrastive approach for learning representations in environments with compositional structure.
+
+* [Link to the paper](https://arxiv.org/abs/1911.12247)
+
+* [Link to the code](https://github.com/tkipf/c-swm).
+
+## Approach
+
+* The training data is in the form of an experience buffer $$B = \{(s_t, a_t, s_{t+1})\}_{t=1}^T$$ of state transition tuples.
+
+* The goal is to learn:
+    
+    * an encoder $$E$$ that maps the observed states $s_t$ (pixel state observations) to latent state $z_t$.
+
+    * a transition model $$T$$ that predicts the dynamics in the hidden state.
+
+* The model defines the enegry of a tuple $$(s_t, a_t, s_{t+1})$$ as $$H = d(z_t + T(z_t, a_t), z_{t+1})$$.
+
+* The model has an inductive bias for modeling the effect of action as translation in the abstract state space.
+
+* An extra hinge-loss term is added: $$max(0, \gamma - d(z^{~}_{t}, z_{t+1}))$$ where $$z^{~}_{t} = E(s^{~}_{t})$$ is a corrputed latent state corresponding to a randomly sampled state $$s^{~}_{t}$$.
+
+## Object-Oriented State Factorization
+
+* The goal is to learn object-oriented representations where each state embedding is structured as a set of objects.
+
+* Assuming the number of object slots to be $$K$$, the latent space, and the action space can be factored into $$K$$ independent latent spaces ($$Z_1 \times ... \times Z_K$$) and action spaces ($$A_1 \times ... \times A_k$$) respectively.
+
+* There are *K* CNN-based object extractors and an MLP-based object encoder.
+
+* The actions are represented as one-hot vectors.
+
+* A fully connected graph is induced over *K* objects (representations) and the transition function is modeled as a Graph Neural Network (GNN) over this graph.
+
+* The transition function produces the change in the latent state representation of each object.
+
+* The factorization can be taken into account in the loss function by summing over the loss corresponding to each object.
+
+## Environments
+
+* Grid World Environments - 2D shapes, 3D blocks
+
+* Atari games - Pong and Space Invaders
+
+* 3-body physics simulation
+
+## Setup
+
+* Random policy is used to collect the training data.
+
+* Evaluation is performed in the latent space (no reconstruction in the pixel space) using ranking metrics. The observations (to compare against) are randomly sampled from the buffer.
+
+* Baselines - auto-encoder based World Models and [Physics as Inverse Graphics model](https://arxiv.org/abs/1905.11169).
+
+## Results
+
+* In the grid-world environments, C-SWM models the latent dynamics almost perfectly.
+
+* Removing either the state factorization or the GNN transition model hurts the performance.
+
+* C-SWM performs well on Atari as well but the results tend to have high variance.
+
+* The optimal values of $K$ should be obtained by hyperparameter tuning.
+
+* For the 3-body physics tasks, both the baselines and proposed models work quite well.
+
+* Interestingly, the paper has a section on limitations:
+    
+    * The object extractor module can not disambiguate between multiple instances of the same object (in a scene).
+
+    * The current formulation of C-SWM can only be used with deterministic environments.
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