Training reinforcement learning (RL) models comes with several challenges.
Sample inefficiency: RL agents often require large amounts of interaction with the environment to learn effective policies. This can be computationally expensive, especially in complex environments. Techniques like experience replay and off-policy learning help mitigate this, but sample inefficiency remains a key challenge.
Exploration vs. exploitation: Balancing exploration (trying new actions) and exploitation (choosing known good actions) is crucial. If an agent explores too much, it might take unnecessary risks, and if it exploits too much, it may not discover better strategies.
Delayed rewards: In many environments, the rewards for actions are delayed, which can make it difficult for the agent to learn which actions are truly valuable. Addressing credit assignment and managing temporal dependencies, like in Temporal Difference (TD) learning, is an ongoing challenge.