retrying

This paper introduces ReMax, a new objective for reinforcement learning that induces exploration as an emergent property by evaluating policies based on expected maximum return over multiple samples, without explicit exploration bonuses. The authors derive a policy gradient formulation and propose RePPO, a PPO variant that achieves efficient exploration on MinAtar and Craftax benchmarks.