Questions tagged [ddpg]

For questions related to the reinforcement learning algorithm called Deep Deterministic Policy Gradient (DDPG).

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Policy gradient methods for continuous action space

I have a problem I would like to tackle with RL but I am not sure if it is even doable. My agent has to figure out how to fill a very large vector (let's say from 600 to 4000 in the most complex ...
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1answer
69 views

Is DDPG just for deterministic environments?

I want to develop an AI for continuous space. I reached to DDPG algorithm that takes actions deterministically. If DDPG takes actions deterministically, should the environment also be deterministic? ...
2
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1answer
31 views

Training actor-critic algorithms in games with opponents

I am wondering how am I supposed to train a model using actor/critic algorithms in environments with opponents. I tried the followings (using A3C and DDPG): Play against random player. I had rather ...
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0answers
41 views

Should noise (such as OU) be decreased over time in actor / critic algorithms?

In most of RL algorithms I saw, there is a coefficient that reduces actions exploration over time, to help convergence. But in Actor-Critic, or other algorithms (A3C, DDPG, ...) used in continuous ...
2
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2answers
366 views

How large should the replay buffer be?

I'm learning DDPG algorithm by following the following link: Open AI Spinning Up document on DDPG, where it is written In order for the algorithm to have stable behavior, the replay buffer should ...
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0answers
28 views

Should we multiply the target of actor by the importance sampling ratio when prioritized replay is applied to DDPG?

According to PER, we have to multiply the $Q$ error $\delta_i$ by the importance sampling ratio to correct the bias introduced by the imbalance sampling of PER, where importance sampling ratio is ...
2
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0answers
71 views

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 ...