Adnan KhanAdnanProduct & AI
San Francisco Bay Area
Get in touch

Source note3 min read

Knowledge- and Gradient-Guided Reinforcement Learning for Parametrized Action Markov Decision Processes

Source

arXiv

arxiv.org/abs/2607.12924v1

Read the source

Source

Plain-Language Brief

The paper introduces a new reinforcement learning algorithm called Neuro-Symbolic Knowledge- and Gradient-Guided Reinforcement Learning (KGRL). This algorithm uses domain knowledge to improve the sample efficiency of training reinforcement learning agents in Parametrized Action Markov Decision Processes (PAMDP). It prunes non-applicable actions from the decision space and constrains parameter spaces, making it more effective than current state-of-the-art RL baselines for PAMDPs.

What the Document Says

  • The paper discusses Reinforcement Learning in Parametrized Action Markov Decision Processes (PAMDP).
  • In PAMDP, each decision consists of a symbolic action and numerical parameters.
  • Reinforcement Learning algorithms typically determine parameters with one-shot estimators which makes their training sample inefficient.
  • Most PAMDP environments have explicit but incomplete knowledge such as rules, safety constraints, or expert heuristics, but this knowledge is rarely used to increase the sample-efficiency of training Reinforcement Learning agents.
  • The paper proposes a new algorithm called Neuro-Symbolic Knowledge- and Gradient-Guided Reinforcement Learning (KGRL).
  • KGRL uses domain knowledge in a Datalog knowledge base to prune non-applicable actions from the decision space and constrain parameter spaces.
  • A gradient-based parameter refinement loop is used to estimate optimal parameters during training and deployment of the agent.
  • KGRL records activated rules along the trajectory, providing local procedural explanations for action pruning and parameter constraints.
  • KGRL guides the agent's exploration and deployment towards feasible and constraint-aware decisions.
  • KGRL increases sample efficiency during training.
  • KGRL outperforms state-of-the-art RL baselines for PAMDPs in both, sample efficiency and episodic return.

Key Concepts Mentioned

  • Reinforcement Learning
  • Parametrized Action Markov Decision Processes (PAMDP)
  • Neuro-Symbolic Knowledge- and Gradient-Guided Reinforcement Learning (KGRL)
  • Datalog knowledge base

Claims Made by the Document

  • Reinforcement Learning algorithms determine parameters with one-shot estimators which makes their training sample inefficient.
  • Most PAMDP environments have explicit but incomplete knowledge that is rarely used to increase the sample-efficiency of training Reinforcement Learning agents.
  • KGRL uses domain knowledge to prune non-applicable actions and constrain parameter spaces, improving sample efficiency during training.
  • KGRL outperforms state-of-the-art RL baselines for PAMDPs.

Evidence Used

  • The text discusses the inefficiency of Reinforcement Learning algorithms due to one-shot estimators and lack of knowledge utilization.
  • The text describes how KGRL uses domain knowledge to prune non-applicable actions and constrain parameter spaces.
  • The text states that KGRL outperforms state-of-the-art RL baselines for PAMDPs.

Methods / Approach

  • Reinforcement Learning
  • Parametrized Action Markov Decision Processes
  • Neuro-Symbolic Knowledge- and Gradient-Guided Reinforcement Learning (KGRL)
  • Datalog knowledge base
  • gradient-based parameter refinement loop

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

In this paper, we study Reinforcement Learning in Parametrized Action Markov Decision Processes (PAMDP), where each decision consists of a symbolic action and numerical parameters. In such settings Reinforcement Learning algorithms typically determine parameters with one-shot estimators, which makes their training sample inefficient. Though in most PAMDP environments explicit but incomplete knowledge (e.g., rules, safety constraints, or expert heuristics) is available, it is rarely directly used to increase the sample-efficiency of training Reinforcement Learning agents. We step into this gap and propose our novel Neuro-Symbolic Knowledge- and Gradient-Guided Reinforcement Learning (KGRL) algorithm. KGRL uses domain knowledge in a Datalog knowledge base to derive the set of applicable actions and feasible parameters for a given state. This allows it to prune non-applicable actions from the decision-space and constrain the parameter spaces of the remaining actions. We then use a gradient-based parameter refinement loop to estimate the optimal parameters during training and deployment of the agent. By recording activated rules along the trajectory, KGRL additionally provides local procedural explanations on the pruning of actions and constraining of parameters. Overall, KGRL guides the agent's exploration and deployment toward feasible and constraint-aware decisions, while increasing sample efficiency during training. KGRL outperforms state-of-the-art RL baselines for PAMDPs in both, sample efficiency and episodic return.