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Found 22 papers, 17 papers with code
Discovering General Reinforcement Learning Algorithms with Adversarial Environment Design
Recently, it has been shown that it is possible to meta-learn update rules, with the hope of discovering algorithms that can perform well on a wide range of RL tasks.
An Investigation of the Bias-Variance Tradeoff in Meta-Gradients
Meta-gradients provide a general approach for optimizing the meta-parameters of reinforcement learning (RL) algorithms.
Model-Value Inconsistency as a Signal for Epistemic Uncertainty
Unlike prior work which estimates uncertainty by training an ensemble of many models and/or value functions, this approach requires only the single model and value function which are already being learned in most model-based reinforcement learning algorithms.
Model-based Reinforcement Learning
Rolling Shutter Correction
Self-Consistent Models and Values
Learned models of the environment provide reinforcement learning (RL) agents with flexible ways of making predictions about the environment.
Proper Value Equivalence
The value-equivalence (VE) principle proposes a simple answer to this question: a model should capture the aspects of the environment that are relevant for value-based planning.
Model-based Reinforcement Learning
Reinforcement Learning (RL)
PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learning
This allows us to disentangle shared features and dynamics of the environment from agent-specific rewards and policies.
Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
We show in particular that this projection can fail to recover the optimal policy even with access to $Q^*$, which primarily stems from the equal weighting placed on each joint action.
Transient Non-Stationarity and Generalisation in Deep Reinforcement Learning
Non-stationarity can arise in Reinforcement Learning (RL) even in stationary environments.
Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
At the same time, it is often possible to train the agents in a centralised fashion where global state information is available and communication constraints are lifted.
Ranked #6 on
SMAC
on SMAC 6h_vs_8z
Loaded DiCE: Trading off Bias and Variance in Any-Order Score Function Gradient Estimators for Reinforcement Learning
Gradient-based methods for optimisation of objectives in stochastic settings with unknown or intractable dynamics require estimators of derivatives.
Loaded DiCE: Trading off Bias and Variance in Any-Order Score Function Estimators for Reinforcement Learning
Gradient-based methods for optimisation of objectives in stochastic settings with unknown or intractable dynamics require estimators of derivatives.
Growing Action Spaces
In complex tasks, such as those with large combinatorial action spaces, random exploration may be too inefficient to achieve meaningful learning progress.
A Survey of Reinforcement Learning Informed by Natural Language
To be successful in real-world tasks, Reinforcement Learning (RL) needs to exploit the compositional, relational, and hierarchical structure of the world, and learn to transfer it to the task at hand.
The StarCraft Multi-Agent Challenge
In this paper, we propose the StarCraft Multi-Agent Challenge (SMAC) as a benchmark problem to fill this gap.
Ranked #6 on
SMAC
on SMAC 6h_vs_8z
Multi-Agent Common Knowledge Reinforcement Learning
In this paper, we show that common knowledge between agents allows for complex decentralised coordination.
Multi-agent Reinforcement Learning
reinforcement-learning
+3
A Better Baseline for Second Order Gradient Estimation in Stochastic Computation Graphs
To improve the sample efficiency of DiCE, we propose a new baseline term for higher order gradient estimation.
DiCE: The Infinitely Differentiable Monte Carlo Estimator
Lastly, to match the first-order gradient under differentiation, SL treats part of the cost as a fixed sample, which we show leads to missing and wrong terms for estimators of higher-order derivatives.
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
At the same time, it is often possible to train the agents in a centralised fashion in a simulated or laboratory setting, where global state information is available and communication constraints are lifted.
Ranked #1 on
SMAC+
on Off_Near_parallel
Multi-agent Reinforcement Learning
reinforcement-learning
+4
DiCE: The Infinitely Differentiable Monte-Carlo Estimator
Lastly, to match the first-order gradient under differentiation, SL treats part of the cost as a fixed sample, which we show leads to missing and wrong terms for estimators of higher-order derivatives.
TreeQN and ATreeC: Differentiable Tree-Structured Models for Deep Reinforcement Learning
To address these challenges, we propose TreeQN, a differentiable, recursive, tree-structured model that serves as a drop-in replacement for any value function network in deep RL with discrete actions.
Counterfactual Multi-Agent Policy Gradients
COMA uses a centralised critic to estimate the Q-function and decentralised actors to optimise the agents' policies.
Ranked #1 on
SMAC+
on Off_Superhard_parallel
Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning
Many real-world problems, such as network packet routing and urban traffic control, are naturally modeled as multi-agent reinforcement learning (RL) problems.
