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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What makes TRPO an actor-critic method? Where is the critic?
From what I understand, Trust Region Policy Optimization (TRPO) is a modification on Natural Policy Gradient (NPG) that derives the optimal step size $\beta$ from a KL constraint between the new and ...
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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 ...
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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 ...
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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 (...
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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 ...
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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 ...
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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 ...
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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? ...
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