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Unofficial Pytorch code for "Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models"

Results 16 handful-of-trials-pytorch issues
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Bumps [numpy](https://github.com/numpy/numpy) from 1.15.2 to 1.22.0. Release notes Sourced from numpy's releases. v1.22.0 NumPy 1.22.0 Release Notes NumPy 1.22.0 is a big release featuring the work of 153 contributors spread...

dependencies

Bumps [tensorflow](https://github.com/tensorflow/tensorflow) from 1.12.0 to 2.7.2. Release notes Sourced from tensorflow's releases. TensorFlow 2.7.2 Release 2.7.2 This releases introduces several vulnerability fixes: Fixes a code injection in saved_model_cli (CVE-2022-29216) Fixes...

dependencies

Bumps [protobuf](https://github.com/protocolbuffers/protobuf) from 3.6.1 to 3.15.0. Release notes Sourced from protobuf's releases. Protocol Buffers v3.15.0 Protocol Compiler Optional fields for proto3 are enabled by default, and no longer require the...

dependencies

Bumps [ipython](https://github.com/ipython/ipython) from 7.2.0 to 7.16.3. Commits d43c7c7 release 7.16.3 5fa1e40 Merge pull request from GHSA-pq7m-3gw7-gq5x 8df8971 back to dev 9f477b7 release 7.16.2 138f266 bring back release helper from master...

dependencies

Bumps [urllib3](https://github.com/urllib3/urllib3) from 1.24.1 to 1.26.5. Release notes Sourced from urllib3's releases. 1.26.5 :warning: IMPORTANT: urllib3 v2.0 will drop support for Python 2: Read more in the v2.0 Roadmap Fixed...

dependencies

Bumps [py](https://github.com/pytest-dev/py) from 1.7.0 to 1.10.0. Changelog Sourced from py's changelog. 1.10.0 (2020-12-12) Fix a regular expression DoS vulnerability in the py.path.svnwc SVN blame functionality (CVE-2020-29651) Update vendored apipkg: 1.4...

dependencies

Bumps [pygments](https://github.com/pygments/pygments) from 2.3.1 to 2.7.4. Release notes Sourced from pygments's releases. 2.7.4 Updated lexers: Apache configurations: Improve handling of malformed tags (#1656) CSS: Add support for variables (#1633, #1666)...

dependencies

The validation loss was calculating using the whole dataset. Now it is calculated only using 5k random samples

I just run " python mbexp.py -env reacher" with gym 0.17.2, but get the following traceback: Traceback (most recent call last): File "mbexp.py", line 64, in main(args.env, "MPC", args.ctrl_arg, args.override,...