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.

Filter by
Sorted by
Tagged with
0 votes
0 answers
9 views

As someone starting out in RL, could you help me understand the differences between actor-only, critic-only, and actor-critic methods?

I have been reading some medium articles and these three methods pop up a lot. I am wondering what the differences between these are, what are the advantages of one over the other, etc. Also from my ...
user avatar
1 vote
0 answers
20 views

Is it possible to combine two policy-based RL agents?

I am developing an RL agent for a game environment. I have found out that there are two strategies to do well in the game. So I have trained two RL agents using neural networks with distinct reward ...
user avatar
0 votes
1 answer
72 views

How to derive the dual function step by step in relative entropy policy search (REPS)?

TL:DR, (Why) is one of the terms in the expectation not derived properly? Relative entropy policy search or REPS is used to optimize a policy in an MDP. The update step is limited in the policy space (...
user avatar
  • 143
6 votes
1 answer
413 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 ...
user avatar
5 votes
1 answer
211 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 ...
user avatar
  • 287
1 vote
0 answers
65 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 ...
user avatar
  • 281
1 vote
0 answers
33 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? ...
user avatar
  • 362