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Found 13 papers, 4 papers with code
Learning Agile Soccer Skills for a Bipedal Robot with Deep Reinforcement Learning
Although the robots are inherently fragile, minor hardware modifications together with basic regularization of the behavior during training led the robots to learn safe and effective movements while still performing in a dynamic and agile way.
Developing, Evaluating and Scaling Learning Agents in Multi-Agent Environments
The Game Theory & Multi-Agent team at DeepMind studies several aspects of multi-agent learning ranging from computing approximations to fundamental concepts in game theory to simulating social dilemmas in rich spatial environments and training 3-d humanoids in difficult team coordination tasks.
From Motor Control to Team Play in Simulated Humanoid Football
In a sequence of stages, players first learn to control a fully articulated body to perform realistic, human-like movements such as running and turning; they then acquire mid-level football skills such as dribbling and shooting; finally, they develop awareness of others and play as a team, bridging the gap between low-level motor control at a timescale of milliseconds, and coordinated goal-directed behaviour as a team at the timescale of tens of seconds.
Biases for Emergent Communication in Multi-agent Reinforcement Learning
We study the problem of emergent communication, in which language arises because speakers and listeners must communicate information in order to solve tasks.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
A Generalized Training Approach for Multiagent Learning
This paper investigates a population-based training regime based on game-theoretic principles called Policy-Spaced Response Oracles (PSRO).
Emergent Coordination Through Competition
We study the emergence of cooperative behaviors in reinforcement learning agents by introducing a challenging competitive multi-agent soccer environment with continuous simulated physics.
Human-level performance in first-person multiplayer games with population-based deep reinforcement learning
Recent progress in artificial intelligence through reinforcement learning (RL) has shown great success on increasingly complex single-agent environments and two-player turn-based games.
Value-Decomposition Networks For Cooperative Multi-Agent Learning
We study the problem of cooperative multi-agent reinforcement learning with a single joint reward signal.
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Multi-agent Reinforcement Learning
reinforcement-learning
+2
Nesterov's Accelerated Gradient and Momentum as approximations to Regularised Update Descent
We present a unifying framework for adapting the update direction in gradient-based iterative optimization methods.
A Gauss-Newton Method for Markov Decision Processes
In this work we investigate approximate Newton methods for policy optimization in Markov Decision Processes (MDPs).
Deterministic Policy Gradient Algorithms
In this paper we consider deterministic policy gradient algorithms for reinforcement learning with continuous actions.
Modelling transition dynamics in MDPs with RKHS embeddings
For policy optimisation we compare with least-squares policy iteration where a Gaussian process is used for value function estimation.
Online Prediction on Large Diameter Graphs
Current on-line learning algorithms for predicting the labelling of a graph have an important limitation in the case of large diameter graphs; the number of mistakes made by such algorithms may be proportional to the square root of the number of vertices, even when tackling simple problems.
