CODEX: A Cluster-Based Method for Explainable Reinforcement Learning
Despite the impressive feats demonstrated by Reinforcement Learning (RL), these algorithms have seen little adoption in high-risk, real-world applications due to current difficulties in explaining RL agent actions and building user trust. We present Counterfactual Demonstrations for Explanation (CODEX), a method that incorporates semantic clustering, which can effectively summarize RL agent behavior in the state-action space. Experimentation on the MiniGrid and StarCraft II gaming environments reveals the semantic clusters retain temporal as well as entity information, which is reflected in the constructed summary of agent behavior. Furthermore, clustering the discrete+continuous game-state latent representations identifies the most crucial episodic events, demonstrating a relationship between the latent and semantic spaces. This work contributes to the growing body of work that strives to unlock the power of RL for widespread use by leveraging and extending techniques from Natural Language Processing.
Code (1)
Tasks
Clusteringcounterfactualreinforcement-learningReinforcement LearningReinforcement Learning (RL)StarcraftStarcraft IISimilar Papers 제목 키워드 기반
CodeX: Bit-Flexible Encoding for Streaming-based FPGA Acceleration of DNNs
This paper proposes CodeX, an end-to-end framework that facilitates encoding, bitwidth customization, fine-tuning, and implementation of neural networks on FPGA platforms. CodeX incorporates nonlinear encoding to the com…
Reinforcement LearningCoDex: Learning Compositional Dexterous Functional Manipulation without Demonstrations
In this work, we study Compositional Dexterous Functional Object Manipulation (CD-FOM): tasks such as aiming and actuating a spray bottle on a plant or a glue gun on wood, which require both actuating an object's interna…
Reinforcement LearningCodexDB: Generating Code for Processing SQL Queries using GPT-3 Codex
CodexDB is an SQL processing engine whose internals can be customized via natural language instructions. CodexDB is based on OpenAI's GPT-3 Codex model which translates text into code. It is a framework on top of GPT-3 C…
Explainable k-means. Don't be greedy, plant bigger trees!
We provide a new bi-criteria $\tilde{O}(\log^2 k)$ competitive algorithm for explainable $k$-means clustering. Explainable $k$-means was recently introduced by Dasgupta, Frost, Moshkovitz, and Rashtchian (ICML 2020). It …
ClusteringCoDEx: A Comprehensive Knowledge Graph Completion Benchmark
We present CoDEx, a set of knowledge graph completion datasets extracted from Wikidata and Wikipedia that improve upon existing knowledge graph completion benchmarks in scope and level of difficulty. In terms of scope, C…
BenchmarkingKnowledge Graph CompletionKnowledge GraphsLink Prediction+1