2022 Developer Survey is open! Take survey.

Questions tagged [td3]

For questions related to the Twin Delayed Deep Deterministic policy gradient algorithm (TD3).

8 questions with no upvoted or accepted answers
Filter by
Sorted by
Tagged with
2 votes
0 answers
645 views

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 ...
user avatar
  • 167
1 vote
0 answers
20 views

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 ...
user avatar
  • 11
0 votes
0 answers
31 views

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 ...
user avatar
  • 49
0 votes
0 answers
46 views

How to limit the action space and normalize at the same time in PPO?

In PPO (or TD3), how can you both determine the minimum and maximum action the agent can take (for example, between 0 and 1) and also make sure that all the actions sum up to 1? In Python, I can use <...
user avatar
0 votes
0 answers
87 views

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 ...
user avatar
  • 1
0 votes
0 answers
39 views

RL agent policy performs worse than random policy

I am training a trading bot with TD3 and SAC algorithms. During the first 10k steps it takes uniformly random actions before running policy learnt so far. The agent starts to do gradient descent ...
user avatar
  • 167
0 votes
0 answers
46 views

Actor-critic reinforcement learning updates and episode length

I am currently using a TD3 agent-critic network to control a vehicle suspension system, where the reward (or rather a penalty) is based on the vertical acceleration of the mass and is calculated at ...
user avatar
0 votes
0 answers
298 views

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 ...
user avatar