Questions tagged [td3]
For questions related to the Twin Delayed Deep Deterministic policy gradient algorithm (TD3).
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Can action be dominated by state features in actor-critic algorithms?
I have a case where my state consists of relatively large number of features, e.g. 50, whereas my action size is 1. I wonder whether my state features dominate the action in my critic network. I ...
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What is the meaning about the $\alpha$ in TD3 algorithm
I am study the paper with TD3 algorithm.
I am curious about the meaning of $\alpha$ while the paper prove that overestimation will be happened in a critical situation.
The contents about mathematical ...
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If we have a working reward function, would adding another action have a significant effect on the agent performance if task remains the same?
If we have a working reward function, providing the desired behavior and optimal policy in a continuous action/state-space problem, would adding another action significantly affect the possible ...
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Training a RL agent using different data at each episode
I am training a RL agent whose state is composed of two numbers, ranging between 4 ~ 16 and 0 ~ 360. The action is continuous and between 0~90. In real life, the states can be any I am training a TD3 ...
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Is it possible to use Softmax as an activation function for actor (policy) network in TD3 or SAC Reinforcement learning algorithms?
As I understand from literature, normally, the last activation in an actor (policy) network in TD3 and SAC algorithms is a Tanh function, which is scaled by a certain limit.
My action vector is ...
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TD3 sticking to end values [closed]
I am using TD3 on a custom gym environment, but the problem is that the action values stick to the end. Sticking to the end values makes reward negative, to be positive it must find action values ...
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3
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Optimal episode length in reinforcement learning
I have a custom environment for stock trading where an episode can be as long as 2000-3000 steps. I've run several experiments with td3 and sac algorithms, average reward per episode flattens after ...
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Which is the best RL algo for continuous states but discrete action spaces problem
I am trying to train an AI with an environment where the states are continuous but the actions are discrete, that means I can not apply DDPG or TD3.
Can someone please help to let know what should be ...
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