Is Exploration All You Need? Effective Exploration Characteristics for Transfer in Reinforcement Learning

arXiv:2404.02235v1 Announce Type: new
Abstract: In deep reinforcement learning (RL) research, there has been a concerted effort to design more efficient and productive exploration methods while solving sparse-reward problems. These exploration methods often share common principles (e.g., improving diversity) and implementation details (e.g., intrinsic reward). Prior work found that non-stationary Markov decision processes (MDPs) require exploration to efficiently adapt to changes in the environment with online transfer learning. However, the relationship between specific exploration characteristics and effective transfer learning in …

abstract arxiv cs.ai cs.lg design diversity exploration implementation improving intrinsic prior productive reinforcement reinforcement learning research transfer type work