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Found 6 papers, 3 papers with code
Efficient Multi-Task Reinforcement Learning via Selective Behavior Sharing
The ability to leverage shared behaviors between tasks is critical for sample-efficient multi-task reinforcement learning (MTRL).
CoMPS: Continual Meta Policy Search
Beyond simply transferring past experience to new tasks, our goal is to devise continual reinforcement learning algorithms that learn to learn, using their experience on previous tasks to learn new tasks more quickly.
Policy Transfer across Visual and Dynamics Domain Gaps via Iterative Grounding
In this paper, we propose a novel policy transfer method with iterative "environment grounding", IDAPT, that alternates between (1) directly minimizing both visual and dynamics domain gaps by grounding the source environment in the target environment domains, and (2) training a policy on the grounded source environment.
Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning
In this paper, we aim to develop a simple and scalable reinforcement learning algorithm that uses standard supervised learning methods as subroutines.
Ranked #1 on
OpenAI Gym
on Humanoid-v2
Advantage Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning
In this paper, we aim to develop a simple and scalable reinforcement learning algorithm that uses standard supervised learning methods as subroutines.
MCP: Learning Composable Hierarchical Control with Multiplicative Compositional Policies
In this work, we propose multiplicative compositional policies (MCP), a method for learning reusable motor skills that can be composed to produce a range of complex behaviors.
