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Found 8 papers, 5 papers with code
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
We explore deep reinforcement learning methods for multi-agent domains.
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The Option-Critic Architecture
Temporal abstraction is key to scaling up learning and planning in reinforcement learning.
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.
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]).
General Policy Evaluation and Improvement by Learning to Identify Few But Crucial States
In continuous control problems with infinitely many states, our value function minimizes its prediction error by simultaneously learning a small set of `probing states' and a mapping from actions produced in probing states to the policy's return.
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 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.
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.
