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Found 5 papers, 3 papers with code
Learning Symbolic Representations for Reinforcement Learning of Non-Markovian Behavior
Recent work has leveraged Knowledge Representation (KR) to provide a symbolic abstraction of aspects of the state that summarize reward-relevant properties of the state-action history and support learning a Markovian decomposition of the problem in terms of an automaton over the KR.
Noisy Symbolic Abstractions for Deep RL: A case study with Reward Machines
Natural and formal languages provide an effective mechanism for humans to specify instructions and reward functions.
Learning to Follow Instructions in Text-Based Games
Text-based games present a unique class of sequential decision making problem in which agents interact with a partially observable, simulated environment via actions and observations conveyed through natural language.
Challenges to Solving Combinatorially Hard Long-Horizon Deep RL Tasks
Deep reinforcement learning has shown promise in discrete domains requiring complex reasoning, including games such as Chess, Go, and Hanabi.
Interpretable Sequence Classification via Discrete Optimization
Our automata-based classifiers are interpretable---supporting explanation, counterfactual reasoning, and human-in-the-loop modification---and have strong empirical performance.
