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Found 13 papers, 7 papers with code
Continuous Control with Action Quantization from Demonstrations
The proposed approach consists in learning a discretization of continuous action spaces from human demonstrations.
Acme: A Research Framework for Distributed Reinforcement Learning
These implementations serve both as a validation of our design decisions as well as an important contribution to reproducibility in RL research.
Google Research Football: A Novel Reinforcement Learning Environment
Recent progress in the field of reinforcement learning has been accelerated by virtual learning environments such as video games, where novel algorithms and ideas can be quickly tested in a safe and reproducible manner.
Brax -- A Differentiable Physics Engine for Large Scale Rigid Body Simulation
We present Brax, an open source library for rigid body simulation with a focus on performance and parallelism on accelerators, written in JAX.
What Matters for Adversarial Imitation Learning?
To tackle this issue, we implement more than 50 of these choices in a generic adversarial imitation learning framework and investigate their impacts in a large-scale study (>500k trained agents) with both synthetic and human-generated demonstrations.
Episodic Curiosity through Reachability
One solution to this problem is to allow the agent to create rewards for itself - thus making rewards dense and more suitable for learning.
What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study
In recent years, on-policy reinforcement learning (RL) has been successfully applied to many different continuous control tasks.
What Matters for On-Policy Deep Actor-Critic Methods? A Large-Scale Study
In recent years, reinforcement learning (RL) has been successfully applied to many different continuous control tasks.
Agent-Centric Representations for Multi-Agent Reinforcement Learning
Empirically, agent-centric representation learning leads to the emergence of more complex cooperation strategies between agents as well as enhanced sample efficiency and generalization.
Hyperparameter Selection for Imitation Learning
The vast literature in imitation learning mostly considers this reward function to be available for HP selection, but this is not a realistic setting.
Braxlines: Fast and Interactive Toolkit for RL-driven Behavior Engineering beyond Reward Maximization
While reward maximization is at the core of RL, reward engineering is not the only -- sometimes nor the easiest -- way for specifying complex behaviors.
vec2text with Round-Trip Translations
We propose a simple data augmentation technique based on round-trip translations and show in extensive experiments that the resulting vec2text model surprisingly leads to vector spaces that fulfill our four desired properties and that this model strongly outperforms both standard and denoising auto-encoders.
