Multi-Goal Reinforcement Learning

Benchmarks archive 2025-07-28

1 leaderboard table shown for this task, 1 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

2 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Most implemented papers archive 2025-07-28

20 shown of 20 papers with code (34 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Proximal Policy Optimization Algorithms
  20 Jul 2017 188 repositories listed Syntology ran 99 of 176 samples · 77 unverified · 94 pointer-only (licence)
  We propose a new family of policy gradient methods for reinforcement learning, which alternate between sampling data through interaction with the environment, and optimizing a "surrogate" objective function using…
• Playing Atari with Deep Reinforcement Learning
  19 Dec 2013 112 repositories listed Syntology ran 56 of 117 samples · 61 unverified · 56 pointer-only (licence)
  We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning.
• Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research
  26 Feb 2018 28 repositories listed
  The purpose of this technical report is two-fold.
• Maximum Entropy-Regularized Multi-Goal Reinforcement Learning
  21 May 2019 3 repositories listed
  This objective encourages the agent to maximize the expected return, as well as to achieve more diverse goals.
• Intrinsically Motivated Goal Exploration Processes with Automatic Curriculum Learning
  7 Aug 2017 3 repositories listed
  We present an algorithmic approach called Intrinsically Motivated Goal Exploration Processes (IMGEP) to enable similar properties of autonomous learning in machines.
• Grounding Hindsight Instructions in Multi-Goal Reinforcement Learning for Robotics
  8 Apr 2022 2 repositories listed Syntology ran 0 of 1 samples · 1 unverified
  We show that hindsight instructions improve the learning performance, as expected.
• An Open-Source Multi-Goal Reinforcement Learning Environment for Robotic Manipulation with Pybullet
  12 May 2021 2 repositories listed
  This work re-implements the OpenAI Gym multi-goal robotic manipulation environment, originally based on the commercial Mujoco engine, onto the open-source Pybullet engine.
• Maximum Entropy Gain Exploration for Long Horizon Multi-goal Reinforcement Learning
  6 Jul 2020 2 repositories listed Syntology ran 2 of 2 samples · 0 unverified
  What goals should a multi-goal reinforcement learning agent pursue during training in long-horizon tasks?
• An Inductive Bias for Distances: Neural Nets that Respect the Triangle Inequality
  14 Feb 2020 2 repositories listed Syntology ran 5 of 5 samples · 0 unverified · 5 pointer-only (licence)
  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
  12 Dec 2019 2 repositories listed
  Current reinforcement learning (RL) algorithms can be brittle and difficult to use, especially when learning goal-reaching behaviors from sparse rewards.
• FDQN: A Flexible Deep Q-Network Framework for Game Automation
  29 May 2024 1 repository listed
  In reinforcement learning, it is often difficult to automate high-dimensional, rapid decision-making in dynamic environments, especially when domains require real-time online interaction and adaptive strategies such as…
• MRHER: Model-based Relay Hindsight Experience Replay for Sequential Object Manipulation Tasks with Sparse Rewards
  28 Jun 2023 1 repository listed
  Sparse rewards pose a significant challenge to achieving high sample efficiency in goal-conditioned reinforcement learning (RL).
• Bilinear value networks
  28 Apr 2022 1 repository listed
  The dominant framework for off-policy multi-goal reinforcement learning involves estimating goal conditioned Q-value function.
• Multi-Goal Reinforcement Learning environments for simulated Franka Emika Panda robot
  25 Jun 2021 1 repository listed Syntology ran 0 of 2 samples · 2 unverified
  This technical report presents panda-gym, a set Reinforcement Learning (RL) environments for the Franka Emika Panda robot integrated with OpenAI Gym.
• Adversarial Intrinsic Motivation for Reinforcement Learning
  27 May 2021 1 repository listed Syntology ran 0 of 14 samples · 14 unverified
  In this paper, we investigate whether one such objective, the Wasserstein-1 distance between a policy's state visitation distribution and a target distribution, can be utilized effectively for reinforcement learning…
• ROLL: Visual Self-Supervised Reinforcement Learning with Object Reasoning
  13 Nov 2020 1 repository listed
  Current image-based reinforcement learning (RL) algorithms typically operate on the whole image without performing object-level reasoning.
• Counterfactual Data Augmentation using Locally Factored Dynamics
  6 Jul 2020 1 repository listed Syntology ran 6 of 6 samples · 0 unverified
  Many dynamic processes, including common scenarios in robotic control and reinforcement learning (RL), involve a set of interacting subprocesses.
• Learning Discrete State Abstractions With Deep Variational Inference
  9 Mar 2020 1 repository listed Syntology ran 0 of 7 samples · 7 unverified
  In this work, we propose an information bottleneck method for learning approximate bisimulations, a type of state abstraction.
• Bias-Reduced Hindsight Experience Replay with Virtual Goal Prioritization
  14 May 2019 1 repository listed
  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
  15 Oct 2018 1 repository listed
  In open-ended environments, autonomous learning agents must set their own goals and build their own curriculum through an intrinsically motivated exploration.

Syntology lines on 9 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.