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Found 7 papers, 4 papers with code
Policy Evaluation Networks
The core idea of this paper is to flip this convention and estimate the value of many policies, for a single set of states.
The Barbados 2018 List of Open Issues in Continual Learning
We want to make progress toward artificial general intelligence, namely general-purpose agents that autonomously learn how to competently act in complex environments.
Learnings Options End-to-End for Continuous Action Tasks
We present new results on learning temporally extended actions for continuoustasks, using the options framework (Suttonet al.[1999b], Precup [2000]).
When Waiting is not an Option : Learning Options with a Deliberation Cost
Recent work has shown that temporally extended actions (options) can be learned fully end-to-end as opposed to being specified in advance.
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
We explore deep reinforcement learning methods for multi-agent domains.
Investigating Recurrence and Eligibility Traces in Deep Q-Networks
Eligibility traces in reinforcement learning are used as a bias-variance trade-off and can often speed up training time by propagating knowledge back over time-steps in a single update.
The Option-Critic Architecture
Temporal abstraction is key to scaling up learning and planning in reinforcement learning.
