Synergistic Action and Style Priors for Learning Legged Locomotion
The paper addresses brittle locomotion learning in DRL by proposing latent action priors derived from expert demonstrations. This prior allows the agent to learn a compressed, informed representation of expert actions, significantly improving sample efficiency and performance in torque control tasks. Combining these latent actions with style rewards helps the agent better replicate expert behavior.