I Taught an Agent to Act Directly - No Q-Values Needed (Day 6: REINFORCE)
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SERIES: Learning RL and JAX in Public - from zero to DeepMind :) Days 4 and 5 were value-based methods. Q-learning, DQN - the agent learns how good each action is, then picks the best one. The policy is implicit. It falls out of the Q-values as a side effect. Day 6 changes the approach entirely. What if you just... learn the policy directly? That is REINFORCE. And it is the foundation of every modern RL algorithm I care about - PPO, A3C, and GRPO (the algorithm behind DeepSeek-R1 that I built a…
1Key Takeaways
- SERIES: Learning RL and JAX in Public - from zero to DeepMind :) Days 4 and 5 were value-based methods.
- Q-learning, DQN - the agent learns how good each action is, then picks the best one.
- It falls out of the Q-values as a side effect.
- Day 6 changes the approach entirely.
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that sERIES: Learning RL and JAX in Public - from zero to DeepMind :) Days 4 and 5 were value-based methods.
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