21 citations · 52 across the 15 of their papers we have counts for
3 papers · 1 filter
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change
Jonathan Clifford Balloch
Real-world autonomous decision-making systems, from robots to recommendation engines, must operate in environments that change over time. While deep reinforcement learning (RL) has…
Is Exploration All You Need? Effective Exploration Characteristics for Transfer in Reinforcement Learning
Jonathan C. Balloch, Rishav Bhagat, Geigh Zollicoffer +3
In deep reinforcement learning (RL) research, there has been a concerted effort to design more efficient and productive exploration methods while solving sparse-reward problems. Th…
The Role of Exploration for Task Transfer in Reinforcement Learning
Jonathan C Balloch, Julia Kim, and Jessica L Inman +1
The exploration--exploitation trade-off in reinforcement learning (RL) is a well-known and much-studied problem that balances greedy action selection with novel experience, and the…