Comparison

But why so many RL algorithms ?

Different tradeoffs

Sample efficiency

  • How many samples do we need to get a good policy ?

  • Off-policy or On-policy ?

    • Off-policy: able to improve the policy without generating new samples from that policy.

    • On-policy: each time the policy is changes, even a little bit, we need to generate new samples.

But why we would use a less efficient algorithm?

Because sample efficiency is not the only measurement for a RL algorithm, and perhaps the less efficient algorithms are quicker -- wall clock time is not the same as efficiency.

Stability & ease of use

Converge ? Converge to what ? Converge every time ?

Supervised learning is almost always gradient descent, but RL is often not gradient descent. For example, Q-learning is fixed point iteration. And ...

  • Policy gradient

    • The only one that actually performs gradient descent (ascent) on the true objective, but also often the least efficient.

  • Value function fitting

    • At best, minimizes error of fit ("Bellman error", not the same as expected reward)

    • At worst, doesn't optimize anything, not guaranteed to converge to anything in the nonlinear case.

  • Model-based RL

    • Model minimizes error of fit, which will converge.

    • But no guarantee that better model is better policy.

Different assumptions

Stochastic or deterministic/Continuous or discrete/Episodic or infinite horizon

  1. Full observability

    • Generally assumed by value function fitting methods

    • Can be mitigated by adding recurrence

  2. Episodic learning

    • Often assumed by pure policy gradient methods

    • Assumed by some model-based RL methods

  3. Continuity or smoothness

    • Assumed by some continuous value function learning methods

    • Often assumed by some model-based RL methods

Different things are easy or hard in different settings

  • Easier to represent the policy

  • Easier to represent the model

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