Two new research papers explore methods for training reinforcement learning agents to follow complex, temporally extended instructions in sub-symbolic environments. The first paper introduces a method that jointly trains a multi-task policy and a symbol grounder, achieving performance comparable to methods that assume knowledge of true symbol grounding. The second paper proposes a novel approach using structured task representations, conditioning the policy on sequences of Boolean formulae and employing a hierarchical neural architecture with an attention mechanism to reason about future subgoals. Both methods aim to improve zero-shot generalization for instruction following in reinforcement learning. AI
IMPACT These methods could enable more capable AI agents that can understand and execute complex, multi-step instructions in real-world scenarios.
RANK_REASON Two academic papers published on arXiv detailing new methods for reinforcement learning instruction following.
- Andrea Fantin
- arXiv
- linear temporal logic
- Mathias Jackermeier
- Neural Reward Machines
- reinforcement learning
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