Comments (2)
From my understanding, after setting an initial target reward for the entire episode, for each reward we get after performing a step, we do
new_target_reward = target_reward - received_reward_from_env
Is this correct?
from decision-transformer.
- In general there isn't a single way to pick RTG and it depends on the environment. You would probably sweep this value for experiments.
- After every step you subtract the received reward, correct.
Multi-Game Decision Transformers (https://arxiv.org/abs/2205.15241) looks into learning the RTG like a value function, removing the need to specify it manually.
from decision-transformer.
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from decision-transformer.