Reinforcement learning (RL) is a group of algorithms that are reward-oriented, meaning they learn how to act in different states by maximizing the rewards they receive from the environment. A challenging testbed for them are the Atari games that were developed more than 30 years ago, as they provide a … See more RL systems with intrinsic rewards use the unfamiliar states error (Error #1) for exploration and aim to eliminate the effects of stochastic noise (Error #2) and model constraints (Error #3). To do so, the model requires 3 … See more The paper compares, as a baseline, the RND model to state-of-the-art (SOTA) algorithms and two similar models as an ablation test: 1. A standard PPO without an intrinsic … See more The RND model exemplifies the progress that was achieved in recent years in hard exploration games. The innovative part of the model, the fixed and target networks, is promising thanks to its simplicity (implementation and … See more WebCuriosity-driven Exploration in Sparse-reward Multi-agent Reinforcement Learning have some drawbacks, such as derailment and detachment. Derailment describes a situation that the agent finds it hard to get back to the frontier exploration in the next episode since the intrinsic motivation rewards the seldom visited states.
Curiosity-driven Exploration in Sparse-reward Multi-agent …
WebNov 12, 2024 · The idea of curiosity-driven learning is to build a reward function that is intrinsic to the agent (generated by the agent itself). That is, the agent is a self-learner, as he is both the student and its own feedback teacher. To generate this reward, we introduce the intrinsic curiosity module (ICM). But this technique has serious drawbacks ... on their end 意味
Extrinsic vs. Intrinsic Motivation: What
WebHis first curiosity- driven, creative agents [1,2] (1990) used an adaptive predictor or data compressor to predict the next input, given some history of actions and inputs. The action- generating, reward- maximizing controller got rewarded for action sequences provoking still unpredictable inputs. WebJan 6, 2024 · The idea that curiosity aligns with reward-based learning has been supported by a growing body of research. One study by Matthias Gruber and his colleagues at the … WebMar 9, 2024 · If we’re driven by an interest that pulls us in, that’s Littman’s I or interest curiosity. If we’re driven by the restless, itchy, need to know state, that’s D or … on their end