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Questions tagged [policy-gradients]

For questions related to policy gradient algorithms (or, in short, "policy gradients"), in reinforcement learning, that is, algorithms that attempt to directly estimate the policy (without first attempting to estimate value functions).

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Reinforcement Learning with more actions than states

I have read a lot about RL recently. As far as I understood, most RL applications have much more states than there are actions to choose from. I am thinking about using RL for a problem where I have ...
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Why can we update the policy and value function at the same time in PPO?

In PPO, every epoch runs many gradient descent iterations to update the policy parameters and the value function. It's not clear to me why we can update both the policy and value at the same time. ...
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Calculating gradient for log policy when variance is not constant

I've noticed that when modelling a continuous action space, the default thing to do is to estimate a mean and a variance where each is parameterized by a neural network or some other model. I also ...
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1answer
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Meaning of Actor Output in Actor Critic Reinforcement Learning

In actor critic, The equations for calculating the loss in actor critic are an actor loss (parameterized by $\theta$) $$log[\pi_\theta(s_t,a_t)]Q_w(s_t,a_t)$$ and a critic loss (parameterized by ...
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1answer
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What information should be cached in experience replay for actor-critic?

Experience replay is buffer (or a "memory") of transactions $e_t = (s_t, a_t, r_t, s_{t+1})$. The equations for calculating the loss in actor critic are an actor loss (parameterized by $\theta$) $$...
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Can I use deterministic policy gradient methods for stochastic policy learning?

Can I treat a stochastic policy (over a finite action space of size $n$) as a deterministic policy (in the set of probability distribution in $\mathbb{R}^n$)? It seems to me that nothing is broken ...
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1answer
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Neural network with logical hidden layer - how to train it? Is it policy gradient problem? Chaining NNs?

I am doing neural machine translation task from language S to language T via interlingua L. So - there is the structure: ...
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How to include exploration in Gaussian policy

When dealing with continuous action spaces, a common choice when designing a policy in policy gradient methods is to learn mean and variance of actions for a specific state and then simply sample from ...
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Impact of Varying Length Trajectories on Policy Gradient Optimization

As the question states, I am wondering how, if at all, a varying length of a trajectory (series of state,action pairs) will impact training/performance of policy gradient algorithms such as PPO, TRPO ...
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1answer
42 views

Can gradient descent training be used for nonsmooth loss functions?

I have non-smooth loss function - e.g. loss(x)=min(x, 0.5). Can gradient descent be used for training neural networks with such functions. Can gradient descent be used for fairly general, ...
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1answer
60 views

Why does the “reward to go” trick in policy gradient methods work?

In policy gradient method, there's a trick to reduce a variance of policy gradient. We use causality, and remove part of the sum over rewards so that only actions happened after the reward are taken ...
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What is the relation between Q-learning and policy gradients methods?

As far as I understand, Q-learning and policy gradients (PG) are the two major approaches used to solve RL problems. While Q-learning aims to predict the reward of a certain action taken in a certain ...