Successor-Predecessor Intrinsic Exploration Changmin Y u 1,2 Neil Burgess
–Neural Information Processing Systems
Exploration is essential in reinforcement learning, particularly in environments where external rewards are sparse. Here we focus on exploration with intrinsic rewards, where the agent transiently augments the external rewards with self-generated intrinsic rewards.
Neural Information Processing Systems
Feb-17-2026, 17:12:30 GMT
- Country:
- Europe > United Kingdom
- England
- Cambridgeshire > Cambridge (0.04)
- Greater London > London (0.04)
- England
- North America > United States (0.04)
- Europe > United Kingdom
- Industry:
- Leisure & Entertainment > Games (0.47)
- Technology: