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Found 6 papers, 4 papers with code
Explicit Recall for Efficient Exploration
In this paper, we advocate the use of explicit memory for efficient exploration in reinforcement learning.
APPL: A Prompt Programming Language for Harmonious Integration of Programs and Large Language Model Prompts
Large Language Models (LLMs) have become increasingly capable of handling diverse tasks with the aid of well-crafted prompts and integration of external tools, but as task complexity rises, the workflow involving LLMs can be complicated and thus challenging to implement and maintain.
Identifying the Risks of LM Agents with an LM-Emulated Sandbox
Alongside the emulator, we develop an LM-based automatic safety evaluator that examines agent failures and quantifies associated risks.
The Scattering Compositional Learner: Discovering Objects, Attributes, Relationships in Analogical Reasoning
In this work, we focus on an analogical reasoning task that contains rich compositional structures, Raven's Progressive Matrices (RPM).
Neural Logic Machines
We propose the Neural Logic Machine (NLM), a neural-symbolic architecture for both inductive learning and logic reasoning.
Universal Agent for Disentangling Environments and Tasks
Recent state-of-the-art reinforcement learning algorithms are trained under the goal of excelling in one specific task.
Hierarchical Reinforcement Learning
reinforcement-learning
+2
