Research map / Reinforcement learning / Visual and representation RL
Discovering symbolic policies with deep reinforcement learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Adding hierarchical entropy regularizer (weights decaying with step) to RNN policy gradient | DSP symbolic policy search · InvertedPendulumSwingup | token-level empirical entropy | Helped |
| Adding soft length prior to initial RNN distribution | DSP expression length distribution · expression length prior | fraction of expressions at max length | Helped |
| Replace regression-based policy distillation with DSP reward-driven search | symbolic policy learning · CartPole, Pendulum, InvDoublePend, LunarLander, Hopper, BipedalWalker | episodic reward | Helped |
| Constant optimization applied to best symbolic policy | DSP · 8 environments | average normalized episodic reward | Mixed |
Closest papers by meaning
- BlendRL: A Framework for Merging Symbolic and Neural Policy Learning (ICLR 2025 · similarity 0.88)
- End-to-End Neuro-Symbolic Reinforcement Learning with Textual Explanations (ICML 2024 · similarity 0.88)
- Mitigating Information Loss in Tree-Based Reinforcement Learning via Direct Optimization (ICLR 2025 · similarity 0.88)
- The Ladder in Chaos: Improving Policy Learning by Harnessing the Parameter Evolving Path in A Low-dimensional Space (NeurIPS 2024 · similarity 0.87)
- Analyzing Generalization in Policy Networks: A Case Study with the Double-Integrator System (AAAI 2024 · similarity 0.87)
- Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning (AAAI 2025 · similarity 0.87)
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