5 votes

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? One practical benefit is that critics can use TD learning to bootstrap, allowing them to ...
Neil Slater's user avatar
  • 32.1k
3 votes

How can the Cart Pole problem be a continuing task?

It's a continuing task in that, after failure, the agent always gets a reward of $0$ at each time-step ad infinitum. From the book: we could treat pole-balancing as a continuing task, using ...
Bijay Gurung's user avatar
2 votes
Accepted

For continuing tasks, is the choice of episode length completely arbitrary?

There are things that impact ideal pseudo-episode length for learning continuing (non-episodic) environments: Start state. The start state of a continuing environment may be special in some way and ...
Neil Slater's user avatar
  • 32.1k
1 vote

Predicting continous value with CNN (prediction of fruit maturity)

but I have been told that neural networks aren't made to predict values in that way, they really are best suited for classification into discrete classes I don't agree with this statement. I already ...
Marcel_marcel1991's user avatar
1 vote

How can the Cart Pole problem be a continuing task?

From Sutton & Barto's book (p. 56) Example 3.4: Pole-Balancing The objective in this task is to apply forces to a cart moving along a track so as to keep a pole hinged to the cart from falling ...
mimoralea's user avatar
  • 111
1 vote

How can the Cart Pole problem be a continuing task?

The key is that reinforcement learning through something like, say, SARSA, works by splitting up the state space into discrete points, and then trying to learn the best action at every point. To do ...
John Doucette's user avatar

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