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Found 6 papers, 1 papers with code
Preference-conditioned Pixel-based AI Agent For Game Testing
This paper addresses these limitations by proposing an agent design that mainly depends on pixel-based state observations while exploring the environment conditioned on a user's preference specified by demonstration trajectories.
Asking Before Acting: Gather Information in Embodied Decision Making with Language Models
With strong capabilities of reasoning and a broad understanding of the world, Large Language Models (LLMs) have demonstrated immense potential in building versatile embodied decision-making agents capable of executing a wide array of tasks.
An Adaptive Deep RL Method for Non-Stationary Environments with Piecewise Stable Context
One of the key challenges in deploying RL to real-world applications is to adapt to variations of unknown environment contexts, such as changing terrains in robotic tasks and fluctuated bandwidth in congestion control.
Distributional Reinforcement Learning for Multi-Dimensional Reward Functions
To fully inherit the benefits of distributional RL and hybrid reward architectures, we introduce Multi-Dimensional Distributional DQN (MD3QN), which extends distributional RL to model the joint return distribution from multiple reward sources.
Distributional Reinforcement Learning
reinforcement-learning
+2
Demonstration Actor Critic
One approach leverages demonstration data in a supervised manner, which is simple and direct, but can only provide supervision signal over those states seen in the demonstrations.
Independence-aware Advantage Estimation
Most of existing advantage function estimation methods in reinforcement learning suffer from the problem of high variance, which scales unfavorably with the time horizon.
