4 papers
Investigating Action Encodings in Recurrent Neural Networks in Reinforcement Learning
Matthew Schlegel, Volodymyr Tkachuk, Adam White +1
Building and maintaining state to learn policies and value functions is critical for deploying reinforcement learning (RL) agents in the real world. Recurrent neural networks (RNNs…
Mitigating Value Hallucination in Dyna Planning via Multistep Predecessor Models
Farzane Aminmansour, Taher Jafferjee, Ehsan Imani +3
Dyna-style reinforcement learning (RL) agents improve sample efficiency over model-free RL agents by updating the value function with simulated experience generated by an environme…
Trajectory-Aware Eligibility Traces for Off-Policy Reinforcement Learning
Brett Daley, Martha White, Christopher Amato +1
Off-policy learning from multistep returns is crucial for sample-efficient reinforcement learning, but counteracting off-policy bias without exacerbating variance is challenging. C…
Empirical Design in Reinforcement Learning
Andrew Patterson, Samuel Neumann, Martha White +1
Empirical design in reinforcement learning is no small task. Running good experiments requires attention to detail and at times significant computational resources. While compute r…