MuJoCo

Benchmarks archive 2025-07-28

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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

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Most implemented papers archive 2025-07-28

30 shown of 293 papers with code (677 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.

• Simple random search provides a competitive approach to reinforcement learning
  19 Mar 2018 26 repositories listed Syntology ran 3 of 15 samples · 12 unverified · 3 pointer-only (licence)
  A common belief in model-free reinforcement learning is that methods based on random search in the parameter space of policies exhibit significantly worse sample complexity than those that explore the space of actions.
• The StarCraft Multi-Agent Challenge
  11 Feb 2019 23 repositories listed Syntology ran 6 of 15 samples · 9 unverified · 13 pointer-only (licence)
  In this paper, we propose the StarCraft Multi-Agent Challenge (SMAC) as a benchmark problem to fill this gap.
• Evolution Strategies as a Scalable Alternative to Reinforcement Learning
  10 Mar 2017 23 repositories listed Syntology ran 7 of 29 samples · 22 unverified · 1 pointer-only (licence)
  We explore the use of Evolution Strategies (ES), a class of black box optimization algorithms, as an alternative to popular MDP-based RL techniques such as Q-learning and Policy Gradients.
• Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning
  23 Sep 2021 11 repositories listed Syntology ran 2 of 5 samples · 3 unverified
  In this paper, we extend the theory of trust region learning to MARL.
• Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning
  8 Aug 2017 9 repositories listed Syntology ran 2 of 2 samples · 0 unverified · 2 pointer-only (licence)
  Model-free deep reinforcement learning algorithms have been shown to be capable of learning a wide range of robotic skills, but typically require a very large number of samples to achieve good performance.
• DeepMind Control Suite
  2 Jan 2018 8 repositories listed
  The DeepMind Control Suite is a set of continuous control tasks with a standardised structure and interpretable rewards, intended to serve as performance benchmarks for reinforcement learning agents.
• Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation
  17 Aug 2017 8 repositories listed Syntology ran 1 of 1 samples · 0 unverified
  In this work, we propose to apply trust region optimization to deep reinforcement learning using a recently proposed Kronecker-factored approximation to the curvature.
• robosuite: A Modular Simulation Framework and Benchmark for Robot Learning
  25 Sep 2020 7 repositories listed Syntology ran 1 of 3 samples · 2 unverified · 1 pointer-only (licence)
  robosuite is a simulation framework for robot learning powered by the MuJoCo physics engine.
• Randomized Ensembled Double Q-Learning: Learning Fast Without a Model
  15 Jan 2021 6 repositories listed Syntology ran 4 of 5 samples · 1 unverified · 3 pointer-only (licence)
  Using a high Update-To-Data (UTD) ratio, model-based methods have recently achieved much higher sample efficiency than previous model-free methods for continuous-action DRL benchmarks.
• SQIL: Imitation Learning via Reinforcement Learning with Sparse Rewards
  27 May 2019 5 repositories listed
  Theoretically, we show that SQIL can be interpreted as a regularized variant of BC that uses a sparsity prior to encourage long-horizon imitation.
• Comparing the Efficacy of Fine-Tuning and Meta-Learning for Few-Shot Policy Imitation
  23 Jun 2023 4 repositories listed
  Despite its simplicity this baseline is competitive with meta-learning methods on a variety of conditions and is able to imitate target policies trained on unseen variations of the original environment.
• Multi-Agent Constrained Policy Optimisation
  6 Oct 2021 4 repositories listed Syntology ran 1 of 7 samples · 6 unverified · 1 pointer-only (licence)
  To fill these gaps, in this work, we formulate the safe MARL problem as a constrained Markov game and solve it with policy optimisation methods.
• Improving Sample Efficiency in Model-Free Reinforcement Learning from Images
  2 Oct 2019 4 repositories listed Syntology ran 5 of 6 samples · 1 unverified
  A promising approach is to learn a latent representation together with the control policy.
• Self-Imitation Learning
  14 Jun 2018 4 repositories listed Syntology ran 1 of 6 samples · 5 unverified · 3 pointer-only (licence)
  This paper proposes Self-Imitation Learning (SIL), a simple off-policy actor-critic algorithm that learns to reproduce the agent's past good decisions.
• Balance Reward and Safety Optimization for Safe Reinforcement Learning: A Perspective of Gradient Manipulation
  2 May 2024 3 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  Ensuring the safety of Reinforcement Learning (RL) is crucial for its deployment in real-world applications.
• EnvPool: A Highly Parallel Reinforcement Learning Environment Execution Engine
  21 Jun 2022 3 repositories listed Syntology ran 1 of 5 samples · 4 unverified
  EnvPool is open-sourced at https://github.
• Rank the Episodes: A Simple Approach for Exploration in Procedurally-Generated Environments
  20 Jan 2021 3 repositories listed Syntology ran 14 of 28 samples · 14 unverified
  Unfortunately, methods based on intrinsic rewards often fall short in procedurally-generated environments, where a different environment is generated in each episode so that the agent is not likely to visit the same…
• Reinforcement Learning with Random Delays
  6 Oct 2020 3 repositories listed Syntology ran 2 of 9 samples · 7 unverified · 1 pointer-only (licence)
  Action and observation delays commonly occur in many Reinforcement Learning applications, such as remote control scenarios.
• How to Learn a Useful Critic? Model-based Action-Gradient-Estimator Policy Optimization
  29 Apr 2020 3 repositories listed Syntology ran 1 of 6 samples · 5 unverified · 1 pointer-only (licence)
  Deterministic-policy actor-critic algorithms for continuous control improve the actor by plugging its actions into the critic and ascending the action-value gradient, which is obtained by chaining the actor's Jacobian…
• FACMAC: Factored Multi-Agent Centralised Policy Gradients
  14 Mar 2020 3 repositories listed Syntology ran 1 of 4 samples · 3 unverified
  We propose FACtored Multi-Agent Centralised policy gradients (FACMAC), a new method for cooperative multi-agent reinforcement learning in both discrete and continuous action spaces.
• Distributional Soft Actor-Critic: Off-Policy Reinforcement Learning for Addressing Value Estimation Errors
  9 Jan 2020 3 repositories listed
  In reinforcement learning (RL), function approximation errors are known to easily lead to the Q-value overestimations, thus greatly reducing policy performance.
• VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning
  18 Oct 2019 3 repositories listed Syntology ran 3 of 3 samples · 0 unverified · 3 pointer-only (licence)
  Trading off exploration and exploitation in an unknown environment is key to maximising expected return during learning.
• Boosting Soft Actor-Critic: Emphasizing Recent Experience without Forgetting the Past
  10 Jun 2019 3 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  The ERE algorithm samples more aggressively from recent experience, and also orders the updates to ensure that updates from old data do not overwrite updates from new data.
• Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations
  12 Apr 2019 3 repositories listed Syntology ran 2 of 3 samples · 1 unverified
  A critical flaw of existing inverse reinforcement learning (IRL) methods is their inability to significantly outperform the demonstrator.
• Learnings Options End-to-End for Continuous Action Tasks
  30 Nov 2017 3 repositories listed
  We present new results on learning temporally extended actions for continuoustasks, using the options framework (Suttonet al.[1999b], Precup [2000]).
• A Bayesian Approach to Robust Inverse Reinforcement Learning
  15 Sep 2023 2 repositories listed Syntology ran 1 of 1 samples · 0 unverified
  We consider a Bayesian approach to offline model-based inverse reinforcement learning (IRL).
• Explaining RL Decisions with Trajectories
  6 May 2023 2 repositories listed Syntology ran 2 of 3 samples · 1 unverified · 3 pointer-only (licence)
  To do so, we encode trajectories in offline training data individually as well as collectively (encoding a set of trajectories).
• When Demonstrations Meet Generative World Models: A Maximum Likelihood Framework for Offline Inverse Reinforcement Learning
  15 Feb 2023 2 repositories listed
  Offline inverse reinforcement learning (Offline IRL) aims to recover the structure of rewards and environment dynamics that underlie observed actions in a fixed, finite set of demonstrations from an expert agent.
• Online Reinforcement Learning in Non-Stationary Context-Driven Environments
  4 Feb 2023 2 repositories listed Syntology ran 1 of 6 samples · 5 unverified · 5 pointer-only (licence)
  We present Locally Constrained Policy Optimization (LCPO), an online RL approach that combats CF by anchoring policy outputs on old experiences while optimizing the return on current experiences.
• On the Effect of Pre-training for Transformer in Different Modality on Offline Reinforcement Learning
  17 Nov 2022 2 repositories listed Syntology ran 6 of 14 samples · 8 unverified
  We empirically investigate how pre-training on data of different modalities, such as language and vision, affects fine-tuning of Transformer-based models to Mujoco offline reinforcement learning tasks.

Syntology lines on 24 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.