Learn Before
Reward Shaping as a Solution for Sparse Rewards
Reward shaping is a technique used to address the challenge of sparse rewards by providing more frequent, intermediate feedback to an agent. As proposed by Andrew Ng, it involves augmenting the original reward function with a potential-based function that depends only on the state. This addition guides the agent's learning without changing the optimal policy, helping to solve problems like meaningless iteration that can arise from delayed rewards.
0
3
Contributors are:
Who are from:
Tags
Data Science
Ch.4 Alignment - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Related
Optimal Reward Problem(ORP)
Abnormal Behavior Types Due to Improper Reward Setting
Reward Construction Direction without a Prior Estimate
Reward Shaping as a Solution for Sparse Rewards
Dense vs. Sparse Rewards
Reward Shaping as a Solution for Sparse Rewards
Transforming Sparse Rewards into Dense Supervision Signals
An AI is being trained to generate a multi-paragraph summary of a long document. The AI writes the summary one sentence at a time. A quality score is given only after the entire summary is complete. For each individual sentence generated before the final one, the score is zero. What is the most significant learning difficulty the AI will face due to this scoring method?
Training an Agent for a Text-Based Game
Credit Assignment in AI Poetry Generation
Methods for Mitigating Sparse Rewards
Learn After
Reward Shaping Formula
An agent is being trained to navigate a complex maze. It receives a large positive reward (+100) only upon reaching the exit, and a reward of 0 for all other steps. To accelerate learning in this environment with delayed feedback, a developer decides to add an additional, intermediate reward at each step. Which of the following intermediate reward strategies is most likely to guide the agent effectively toward the exit without inadvertently changing the optimal path?
Analyzing Reward Shaping Strategies for Text Summarization
A key advantage of implementing a potential-based reward shaping function is that it fundamentally alters the optimal set of actions an agent should take, thereby simplifying complex problems with sparse rewards.