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Found 3 papers, 1 papers with code
Disentangling Exploration of Large Language Models by Optimal Exploitation
Exploration is a crucial skill for self-improvement and open-ended problem-solving.
Mitigating Information Loss in Tree-Based Reinforcement Learning via Direct Optimization
In this paper, we introduce SYMPOL, a novel method for SYMbolic tree-based on-POLicy RL.
Dynamic Interval Restrictions on Action Spaces in Deep Reinforcement Learning for Obstacle Avoidance
We demonstrate their performance in an obstacle avoidance task and compare the methods to penalties, projection, replacement, as well as discrete and continuous masking from the literature.
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