Multi-Goal Reinforcement Learning
17 papers with code • 0 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Multi-Goal Reinforcement Learning
UniMiB SHAR
bipedal-skills
Latest papers
An Inductive Bias for Distances: Neural Nets that Respect the Triangle Inequality
quasimetric-learning/torch-quasimetric
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ICLR 2020
When defining distances, the triangle inequality has proven to be a useful constraint, both theoretically--to prove convergence and optimality guarantees--and empirically--as an inductive bias.
Learning to Reach Goals via Iterated Supervised Learning
Current reinforcement learning (RL) algorithms can be brittle and difficult to use, especially when learning goal-reaching behaviors from sparse rewards.
Maximum Entropy-Regularized Multi-Goal Reinforcement Learning
ruizhaogit/mep
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This objective encourages the agent to maximize the expected return, as well as to achieve more diverse goals.
Bias-Reduced Hindsight Experience Replay with Virtual Goal Prioritization
filipolszewski/prioritized-her
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We call this property the instructiveness of the virtual goal and define it by a heuristic measure, which expresses how well the agent will be able to generalize from that virtual goal to actual goals.
CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning
In open-ended environments, autonomous learning agents must set their own goals and build their own curriculum through an intrinsically motivated exploration.
Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research
DartEnv/dart-env
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The purpose of this technical report is two-fold.
Intrinsically Motivated Goal Exploration Processes with Automatic Curriculum Learning
We present an algorithmic approach called Intrinsically Motivated Goal Exploration Processes (IMGEP) to enable similar properties of autonomous learning in machines.
