Rudder reward redistribution
Webb30 sep. 2024 · RUDDER has been introduced to identify these steps and then redistribute reward to them, thus immediately giving reward if sub-tasks are solved. Since the problem of delayed rewards is... WebbReward redistribution is our main new concept to achieve expected future rewards equal to zero. We start by introducing MDPs, return-equivalent sequence-Markov decision …
Rudder reward redistribution
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WebbIn contrast to RUDDER, potential-based shaping like\nreward shaping [27], look-ahead advice, and look-back advice [50] use a \ufb01xed reward redistribution.\nMoreover, since these methods keep the original reward, the resulting reward redistribution is not\noptimal, as described in the next section, and learning can still be exponentially slow. WebbFor such complex tasks, the recently proposed RUDDER uses reward redistribution to leverage steps in the Q-function that are associated with accomplishing sub-tasks. However, often only few episodes with high rewards are available as demonstrations since current exploration strategies cannot discover them in reasonable time.
WebbRUDDER uses an LSTM model for reward redistribution via return decomposition. The reward redistribution is the difference of two subsequent predictions of the LSTM model. If a state-action pair increases the prediction of the return, then it is immediately rewarded. Using state-action sub-sequences (s;a) 0:t= (s 0;a Webb14 mars 2024 · If we do reward redistribution for every trajectory, we are converting our SDP to a strictly return equivalent SDP. Optimal Reward Redistribution. How should we do our reward redistribution? This is the main idea as expressed in the paper.
Webb18 juli 2024 · To this end, we propose to use Align-RUDDER as an interpretability method for reinforcement learning. Align-RUDDER is a method based on the recently introduced RUDDER framework, ... Patil, V.P., et al.: Align-rudder: learning from few demonstrations by reward redistribution. arXiv, abs/2009.14108 (2024). CoRR Google Scholar; 46. WebbWe propose RUDDER, which performs reward redistribution by return decomposition and, therefore, overcomes problems of TD and MC stemming from delayed rewards. RUDDER …
Webb1 dec. 2024 · RUDDER [114] redistributes the reward by identifying key steps in the demonstrations and increasing the reward of the respective transition. However, RUDDER uses LSTMs to predict the...
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