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Found 14 papers, 3 papers with code
Learning Robot Soccer from Egocentric Vision with Deep Reinforcement Learning
We apply multi-agent deep reinforcement learning (RL) to train end-to-end robot soccer policies with fully onboard computation and sensing via egocentric RGB vision.
Replay across Experiments: A Natural Extension of Off-Policy RL
Replaying data is a principal mechanism underlying the stability and data efficiency of off-policy reinforcement learning (RL).
Learning Agile Soccer Skills for a Bipedal Robot with Deep Reinforcement Learning
We investigate whether Deep Reinforcement Learning (Deep RL) is able to synthesize sophisticated and safe movement skills for a low-cost, miniature humanoid robot that can be composed into complex behavioral strategies in dynamic environments.
SkillS: Adaptive Skill Sequencing for Efficient Temporally-Extended Exploration
The ability to effectively reuse prior knowledge is a key requirement when building general and flexible Reinforcement Learning (RL) agents.
MO2: Model-Based Offline Options
The ability to discover useful behaviours from past experience and transfer them to new tasks is considered a core component of natural embodied intelligence.
Learning Transferable Motor Skills with Hierarchical Latent Mixture Policies
We demonstrate in manipulation domains that the method can effectively cluster offline data into distinct, executable behaviours, while retaining the flexibility of a continuous latent variable model.
Pick Your Battles: Interaction Graphs as Population-Level Objectives for Strategic Diversity
Strategic diversity is often essential in games: in multi-player games, for example, evaluating a player against a diverse set of strategies will yield a more accurate estimate of its performance.
Behavior Priors for Efficient Reinforcement Learning
In this work we consider how information and architectural constraints can be combined with ideas from the probabilistic modeling literature to learn behavior priors that capture the common movement and interaction patterns that are shared across a set of related tasks or contexts.
Data-efficient Hindsight Off-policy Option Learning
We introduce Hindsight Off-policy Options (HO2), a data-efficient option learning algorithm.
V-MPO: On-Policy Maximum a Posteriori Policy Optimization for Discrete and Continuous Control
Some of the most successful applications of deep reinforcement learning to challenging domains in discrete and continuous control have used policy gradient methods in the on-policy setting.
Information asymmetry in KL-regularized RL
In this work we study the possibility of leveraging such repeated structure to speed up and regularize learning.
Exploiting Hierarchy for Learning and Transfer in KL-regularized RL
As reinforcement learning agents are tasked with solving more challenging and diverse tasks, the ability to incorporate prior knowledge into the learning system and to exploit reusable structure in solution space is likely to become increasingly important.
Hierarchical visuomotor control of humanoids
We aim to build complex humanoid agents that integrate perception, motor control, and memory.
Learning to reinforcement learn
We unpack these points in a series of seven proof-of-concept experiments, each of which examines a key aspect of deep meta-RL.
