I have been experimenting with actor-critic networks such as SAC and TD3 on continuous control tasks and trying to do transfer learning using the trained network to another task with smaller observation and action space.

Would it be possible to do so if i were to save the weights in a dictionary and then load it in the new environment? The inputs to the Actor-Critic network requires a state with different dimensions as well as outputting an actor with different dimensions.

I had some experience doing fine-tuning with transformer models by addind another classifier head and fine-tuning it, but how would i do this with Actor-Critic networks, if the initial layer and final layer does not match with the learned agent.