Questions tagged [policy-based-methods]

For questions about policy-based (or policy search) reinforcement learning (RL) methods (or algorithms), which are RL algorithms that directly learn a policy (rather than first learning a value function). An example of a policy search algorithm is REINFORCE, which falls into the category of "policy gradient" algorithms (or "policy gradients"), which is a subset of policy-based algorithms that uses gradient information to guide the search.

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1answer
251 views

What are the advantages of RL with actor-critic methods over actor-only methods?

In general, what are the advantages of RL with actor-critic methods over actor-only (or policy-based) methods? This is not a comparison with the Q-learning series, but probably a method of learning ...
4
votes
1answer
144 views

Is reinforcement learning only about determining the value function?

I started reading some reinforcement learning literature, and it seems to me that all approaches to solving reinforcement learning problems are about finding the value function (state-value function ...
1
vote
0answers
47 views

What are the disadvantages of actor-only methods with respect to value-based ones?

While the advantages of actor-only algorithms, the ones that search directly the policy without the use of the value function, are clear (possibility of having a continuous action space, a stochastic ...
1
vote
0answers
29 views

Are policy-based methods better than value-based methods only for large action spaces?

In different books on reinforcement learning, policy-based methods are motivated by their ability to handle large (continuous) action spaces. Is this the only motivation for the policy-based methods? ...