Neuro-symbolic AI: Reservoir computing + Reinforcement Learning | Hands-on
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๐ฅ Ever wondered how you can combine neural learning with human-readable logic in one agent; so itโs not just a black box, but something you can inspect, tweak, and explain? In this Neuro-Symbolic AI tutorial, we build a hybrid policy for CartPole that fuses an Echo State Network (ESN) reservoir with a symbolic rule module. No theory overload; just the minimum intuition you need, plus a clean notebook implementation you can reuse for your own projects.
Weโll walk through what reservoir computing is, why ESNs often keep recurrent weights frozen, how to write simple rules that โvoteโ left vs right, and how to combine both neural features and rule features into one policy. Then we train the whole thing using REINFORCE (policy gradient); so you can see the hybrid agent actually improve over time.
๐ป Code on GitHub: https://github.com/frezazadeh/Neuro-Symbolic-Reinforcement-Learning-with-Echo-State-Networks-for-CartPole/blob/main/Neuro_Symbolic_Esn_Cartpole_Tutorial.ipynb
โธป
๐ What Youโll Learn (in this lesson)
โข Neuro-Symbolic AI intuition: what โneural + rulesโ actually means
โข Echo State Networks (ESN): reservoir state, spectral radius, and stable dynamics
โข Symbolic module: readable if/else rules from CartPole signals (x, ฮธ)
โข Feature fusion: concatenate ESN(s) and Rules(s) into one policy input
โข Policy output: logits โ Softmax probabilities โ sampled actions
โข REINFORCE training: log-probs, discounted returns, normalization, Adam updates
โข Practical engineering: Gym/Gymnasium compatibility for reset/step APIs
โข Evaluation: learning curve plotting + how to interpret noisy RL training
โธป
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Why Watch This Video?
โข Beginner-Friendly โ RL + neuro-symbolic explained step-by-step like a story
โข Not a Black Box โ the rule module is interpretable and editable in seconds
โข Real Hybrid Design โ
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